REACH Solutions' Blog Page

  • Nov 10th, 2019

The fundamental elements of IIoT device management

With more and more IIoT devices, changing architectures and data management approaches to turn even more data from this increasing number of IIoT devices into actionable intelligence, the importance of IIoT device management can hardly be underestimated.
Device Management enables device manufacturers to configure millions of devices with unique cryptographic identities and the Device Management connection parameters before they leave the factory. With Device Management you can create, inject and securely store the private keys, certificates, server URL and certificate, connection parameters and firmware update keys necessary to connect to Device Management and manage devices.

The key challenges with regards to IIoT device management
The key challenges with regards to IIoT device management include IIoT security, quick (over-the-air) patching, firmware updating and IIoT device visibility in networks; speed of processing and analyzing data to feed essential business applications in real-time with edge computing, fog computing and the role of artificial intelligence as some important areas.
Moreover, haven’t mentioned the numerous standards and protocols, network and communication methods and so forth, is another key topic, as is scalability in ever larger IIoT projects with more IIoT assets and devices, certainly in the industrial IIoT verticals of Industry 4.0.
At first glance it looks like hard to ensure all of this, however by clever designing of device management can solve your challanges. We have to address four basic device management categories:

  • Provisioning and authentication
  • Configuration and control
  • Monitoring and diagnostics
  • Software updates and maintenance

Provisioning and authentication
Security for IIoT devices is crucial, begin with hardware through to connectivity and into the cloud. Device management provides a wide scale of features which ensure chip-to-cloud security, regardless of the industry and market, allowing OEMs to easily design and deploy more robust IIoT solutions.
Provisioning is the process of enlisting a device into the system. Authentication is the first part of that process, where only devices that present the correct credentials are registered. Every detail of this process can vary widely based on implementation.
With REACH Device Management you can create, inject and securely store the private keys, certificates, server URL and certificate, connection parameters and firmware update keys necessary to connect to Device Management Module and manage devices.

Configuration and control
From time to time, your device will need to be further configured by the operators with attributes such as its name and location and application-specific settings.
For example, a sensor is used to measure the pressure of a certain injector and report that information back to the cloud via a cellular connection. Certain parameters will need to be written once the device is installed, such as the unique ID of sensor. Other configuration settings, such as the amount of time between sending pressure messages, are also determined and programmed into the device.

Monitoring and diagnostics
To avoid unplanned downtime, Device Management can help you maintain your critical devices using model-based predictive maintenance technology. Monitoring and diagnostics are essential to minimize the impact of any device downtime due to software bugs or other unforeseen operational problems.
With the help of REACH you can discover any out of ordinary signs by monitoring compute, storage, networking, and I/O statistics at the task or process level, and comparing those statistics to predefined nominal values. If the CPU utilization goes up to 50 percent in a process that would normally consume 4 percent, then that gives troubleshooters another data point that make identifying the bug way faster. Monitoring network statistics can also point out possible security breaches.

Software updates and maintenance
IIoT devices can be deployed widely and be expected to last many years. During this time new features, bug fixes and updates may be appeared which could extend their useful lifetime. There is a chance that vulnerabilities are discovered which affect common libraries and new threat methods are revealed. In this case, a secure remote update mechanism can protect the investment made in the IIoT device and prevent costly recalls and in-field servicing.
There are several potential levels to software and firmware maintenance. First of all, you must have a process to completely and securely update all the device software, including bootloaders and binary blobs. You might use this to fix a security vulnerability that spread trough the platform firmware. To fix application bugs or add simple feature improvements and save network bandwidth, you may just want to upgrade the main running application software without touching the platform firmware.
The REACH Device Management is a good example as a set of APIs are used to upload the latest software or firmware versions, initiate campaigns targeting specific devices, and monitor the results. REACH Device Management uses these APIs to provide a ready-to-use interface to manage device updates allowing easy access to the update features.

Final thoughts
There is a need to automatically classify devices into states that are contextually dependent on the use case in order to integrate the IIoT solution seamlessly into existing business systems and processes. You also need a solution that can automatically supply these challenges with minimal human load.
That’s why it is worth using platforms like REACH Device Management which provides simple, secure, and flexible IoT management capabilities for a range of device profiles.
Tags :
IIOT Device Production Diagnostic Management Monitor Factory
  • Oct 10th, 2019

Go See, Ask Why, Show Respect*

*These words came from Fujio Cho Chairman of Toyota. The Gemba Walking Concept was developed by Taiichi Ohno, the father of the Toyota Production System (TPS).

Some may be familiar with the above sentence, which is one of the principles of the Lean philosophy. But where exactly do you go, ask the questions, and show respect? The answer lies at the root of the problem, the Gemba. Gemba - "The actual place". This is where work takes place and value is created for the customers. Note that work and value are not always aligned. In a factory the Gemba is in the production hall. The daily problems in the production hall truly tests the agility of our shop floor. These problems affect the budget, the dedicated time available for implementation, and also the quality and performance. Precise knowledge of the activities provides an opportunity to increase the efficiency of the processes, improve the quality and achieve the set goals.

How to get starting with Gemba Walking?
Choose the area you want to investigate and determine the purpose of your visit. For example, how can one create more value with less loss? Remember that all value is the end result of some process and that processes can only produce what they are designed to produce -- never something better but often something worse. Do not miss the customer's perspective. Why do you want to introduce changes in the chosen area? Is it because you want to live up to an expectation? Or want better quality? Lower production cost? Perhaps faster response time to changes? Or maybe higher quality support after shipping the product? It is easier to start exploring details when you keep your goals in front of you.

Production data collection methods
First you have to understand the goal, you need to gather all the relevant data of the area. Choosing the proper data collection method is very important! Let's try to define the metrics in the light of the goals. If you do not scale the right metrics, you do not see the real results clearly either. Start at the end of the process and work back towards the beginning. The final stakeholder is the customer. Observe how customer orders are received, where the scheduling process initiated by these customer orders, and how the orders are handled.

You can examine the relationship between the area in question and the OEE. (Many manufacturing companies use the Overall Equipment Effectiveness as a key performance indicator for their production activities. It is the multiple of Availability Rate × Product Rate × Quality Rate and quantifying the three factors of OEE will help to identify the focus of development actions.)

By examining the Gemba environment we can measure how functional, loss-free and accessible the assembly activities are. We can also see how well the assembly process is equipped with raw material.

Toyota popularised several tools and quality improvement methods, but tools and techniques do not work on their own. The human factor also has to be taken into consideration. The previously mentioned „show respect” philosophy based on its understanding of people and human motivation. By observing the concrete work we can get a better understanding on how to teach workers the step-by-step processes and how to motivate them doing a better job.

Before the Gemba Walking, discuss with the participants how the process on the shop floor should run so you can more easily observe if anything in the process deviates from normal workflow.

A worksheet or checklist based on summary results is an excellent guide to structure and conduct the walk. Keep in mind that in addition to a nicely built roadmap, you will also need good practical observational skills.

Gemba walking is a great opportunity to develop critical thinking for both guided participants and the employees of the shop floor. A well-framed „humble question” helps to develop people’s critical thinking skills. Questions should not be asked to prove people wrong. A good question helps the person discover the answers themselves. Questions should focus around “What?” and "How"? not the "Why". Good example: What is the primary purpose of this work activity? How do you know if you are doing an effective job? How do you measure a successful work day? Wrong way: Are you sure, this is the best way to do your job well? Theres an opportunity after the walk to discuss the "Why" type questions too.

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It is easy to become so conditioned in frequent problems that you don’t even notice them. A second set of eyes, less familiar with the workflows and tasks of your team might be very valuable, especially if you take frequent Gemba walks. Identify who must participate in the Gemba Walking and the role they play. The „Gembutsu” is the target of one's focus for improvement. The „Genjitsu” means „The facts”, which shows what exactly is happening on the shop floor. Importantly, ask the process experts of critical machinery or the maintenance engineer as they can give the most insightful information. The Quality team is responsible for providing the tools, processes, and consulting expertise to support other departments in their quality and process improvement work. In many cases, the lean team has to create the most possible efficient flow in a defined process, by removing bottlenecks and non-value creating activities – and hereby achieve the most stable process.

Ready, steady GO! Go?
Divide the participants into small groups. The main purpose is to have as many different points of views as possible in order to make the best decision.

The Gemba walking starts with the technical team who have bigger overview of the production shop floor (e.g. Production engineer, maintenance engineer, process expert of critical machinery). The second one is the quality team, and the final approvement comes from the lean team. The lean team is responsible for mapping and redesigning the task-flow from start to finish, if they identify waste activities in the process. Before commencing the walk, participants should be aware of the time available in the areas they visit. Make a plan of how the Gemba Walking will proceed.

Everyone needs to focus on the current location during the walk, regardless of their field of expertise. Take a break occasionally, providing an opportunity for open conversations and sharing of observations.

Before taking any actions based on Gemba walking, take some time to review all the information collected. Hold a short session after the walk to discuss the results. Talk about open issues, possible roadblocks and develop action plans as needed.

What's the take away?
Performing Gemba walking on a regular basis will build stable relationships with those who actually do the work and create value. It will also help Identifying problems and taking actions for achieving continuous improvement much faster. You will also be able to clearly communicate goals and objectives, leading to increased employee engagement.
Tags :
IOT Gemba Production Process Value Customer Factory
  • Sep 10th, 2019

Data processing

In the Big Data world, we collect data from the environment, and we would like to find a way to process them. In this blog post, we will discuss the two main processing aspects.

Batch processing

The first, and probably the most familiar technique is batch processing. A batch is a set of data points  collected and grouped together over a given period of time. The duration of the batch varies in length: from months, weeks, days, to hours or even denser processing times, as you will see it later. The length of the period usually determines the amount of data for processing. Over a longer period of time, millions of rows are generated easily. Therefore we would need more time or more computational resource to process them all.

However, the big advantage of batch processing is that we can schedule it at any time of the day. Therefore, performance problems do not occur. Since a batch processing job has a broader time requirement than a stream processing one, we can apply a more complex computation logic. A good example of batch processing is aggregation, where we wish to aggregate small files, generated during the day.

Stream processing

Stream processing works on a continuous data stream and handles data as soon as they arrive in the system. The main difference compared to batch processing is that there is no delay: records are processed as individual pieces rather than as a batch. A data stream has some special characteristics over batch processing. These should be considered during the implementation of a streaming job.

One of them is that we do not have the control over the data input rate. Another point is that data records arrive online, mostly without any control or guarantee on the order. However, some systems support the maintenance of the order and exactly once semantics, such as Apache Kafka [1].The last characteristics of stream processing is that once an element has arrived, we have to manage it. Then it has to be processed, discarded or stored.

A good example of a stream processing job is if you want to store the sensor data of the production robot in real-time. Then this data can be shown on a dashboard.

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Microbatch processing

There is a third concept, which combines the first two: micro-batch processing. This technique is mostly classified as stream processing since it can offer near real-time results, but originally it is based on the batch principles. As an input, it also has a data stream, but instead of immediate processing, it waits for an interval (typically a few seconds or a minute) to collect data (micro-batch) and execute the operation on these batches. A typical microbatch job is when we want to apply a complex logic to the incoming production robot sensor data.

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Batch processing

Apache Hadoop [2] is an open-source Big Data software with the purpose of processing data stored across the cluster. From batch processing point of view, the software has two main components, namely MapReduce and HDFS. MapReduce is a programming model, which enables you to process a huge amount of data in a scalable, distributed and fault-tolerant way. You can find out more about this here. HDFS is a distributed and fault-tolerant storage layer in Hadoop  designed to run on commodity hardware.


The original design of Apache Spark [3] is similar to Hadoop. It also operates on batches, but with very small size (Microbatch). The core element of Spark is the Resilient Distributed Dataset (RDD). This is a fault-tolerant collection of incoming records distributed across the cluster. Spark streaming can be integrated with many sources, including Apache Kafka. Spark receives data from the source, splits them into batches, and then processes them.

Although Hadoop and Spark both work with batches, there is a key difference between them. Spark is 100 times faster in memory than Hadoop. At Spark, this higher performance has been achieved via several implementations. They use their own custom DAG scheduler, a query optimizer, and a physical execution engine.


We can achieve a near real-time system with Apache Spark. Theoretically, it can achieve a 1ms micro batch size, but  due to its operation  it is not effective. Spark collects the records in a buffer, and then processes them with a Spark job. However, each job has to be scheduled and executed (for instance, in every 1 ms), which could cause a significant reduction in the possible throughput. From version 2.3 on, Spark supports a new experimental low-latency processing mode, called Continuous Processing [4].

However, if we do not want to use an experimental feature, Apache Flink [5] should be considered. Apache Flink is a framework and distributed processing engine for stateful computations. In order to achieve near real-time stream processing, Flink uses unbounded data streams (flow of data records) and transformations. When a Flink program gets executed, the engine maps it into streaming dataflows. Each dataflow starts with one or more sources and ends in one or more sinks. The dataflows resemble arbitrary directed acyclic graphs (DAGs). Flink also supports batch processing, in which case we have a bounded stream.
Batch Stream
Control input rate yes no
Data size bound unbound
Control on the order yes no
Achievable latency High A few seconds (<1s) Less than a second (<100ms)
Tools Hadoop Spark (microbatch) Flink(stream)

As you can see, with batch processing we can handle a large batch of already stored data and apply complex logic on it  while a stream processing job can achieve low latency, which is required in a real-time application.

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[1] N. Narkhede, “Exactly once Semantics is Possible: Here’s How Apache Kafka Does it,” Confluent, 30-Jun-2017. [Online]. Available: [Accessed: 26-Jun-2019]. [2] “Apache Hadoop.” [Online]. Available: [Accessed: 27-Jun-2019]. [3] “Apache SparkTM - Unified Analytics Engine for Big Data.” [Online]. Available: [Accessed: 27-Jun-2019]. [4] “Structured Streaming Programming Guide - Spark 2.3.0 Documentation.” [Online]. Available: [Accessed: 26-Jun-2019]. [5] “Apache Flink: Stateful Computations over Data Streams.” [Online]. Available: [Accessed: 26-Jun-2019].

Tags :
IOT Spark Data Process Apache Hadoop Streaming Batch Flink
  • June 10th, 2019

Innovation roulette tables from LP to OJSC

Even the ancient Greeks placed great emphasis on two things: philosophy and the enjoyment of life. Of course, they had time to think, compute, celebrate, and did not have to compete globally. What do we need today from their knowledge? What is certain is that there is no progression without innovation. As there is no innovation without open, accepting and inclusive thinking along with productive debate. Ergo, innovation could not exist without debate culture. In addition, it is very difficult to lure innovation without motivation. However, innovation is needed to stay in international competition, from shoemaker to irrigation equipment manufacturer, from teacher through lawyer to design engineer.

It is already an innovation if we copy others cleverly within frameworks: actually, it is not a basic research, but it can be a fruitful solution. This copy is well-known for Best Practices or industry-specific applications such as Good Manufacturing Practices (GMP). If we go on this path further, we have the option of reverse engineering, that is, decrypting the given device or software in order to ensure compatibility with our own device or software. The question of "ownership" of data is increasingly an issue - it is quite clear in the case of unique databases that it is covered by intellectual property protection. However, in cases where my machine provides data (e.g. temperature value) on industry standard protocol so that it is not possible to read it secondly (for example, because the bus system only supports one master), then the interfacing could be allowed for the reader processing machine installed by another authorized by the original manufacturer. The question is where and how the data is generated. There are a lot of manufacturers (we call them industry) who specialize in deconstructing tools and simply copying them cost-effectively - so we have to deal with them because they have to devote a fraction of the research costs to producing the products.

What is essential for innovation? We need brainstorming and basket of ideas from where we can draw from, and after 9 bad ideas we are expecting the 10th good idea to redeem, which we then try to translate into a business case or business plan. For good ideas and business case making you need to seeing the problem and have to have creativity, critical or improving thinking, time, experience (or on the contrary, virgin hands), viticulturalist, emotional intelligence should be needed the most. In terms of a business case, when we do not have a green light yet, it is particularly important that management understands and supports our business case, whether it is a project outside its field of expertise or not. It is vitally important for innovation, for example, that for an IT project, all the non-IT managers involved know the project, understand the project and see how it can improve its own field. Emotional intelligence needs to be addressed in order to unspoken fears, misunderstandings and feelings are treated not only at the moment of the decision, but throughout the entire duration of the innovation project. It is also necessary to enjoy the work. Those who do not enjoy their work or any aspect of it (e.g. to work in a good team), we cannot expect innovation from them. Do we need operators to innovate and bring new ideas? Obviously, if we don't want to stand at least 4 hours a day next to the machines as a leader. Is it possible to innovate in 8 hours or 3 shifts a day? How should we divide work and family life? There is a widespread belief that a "work-life balance" should be created, i.e. the balance between work and private life. According to recent schools and generations, this is almost impossible and worth thinking about in "work-life coexistence", i.e. in working / life co-operation / co-existence where it is not possible to separate sharply when work is done and when there is privacy. For example, before dinner, I write to someone quickly because of a tomorrow meeting, but after half a minute I have dinner (in fact, I can put the cutlery on the table with my left hand and send the message with my right hand). Unfortunately, or not, innovation requires that the workplace should go beyond, and work on the company's problems after work. If the employees have to worrying about a daily living, family, etc., there will be no free thoughts for the company, and they will even think about their privacy issues during their working hours and create their own private innovations at their workplace. It is therefore important to enable our employees to integrate innovation time into everyday thinking with a complex motivational package (relieving the burden). It is necessary to enjoy life for innovation - it is not possible to innovate by force.

Which innovation is good, which one is worthwhile, which one will make the most, or which will ensure the survival of the company? Some say it is unpredictable, roulette tables, others believe the business case is decisive, yet others believe in management buy-in with the support of the business case (called commitment only). Sure, if we act quickly, we'll quickly find out if the project was successful. If you try and prove the feasibility of an innovation idea with a small investment and a quick pilot project (PoC, the Proof of Concept), you can quickly move on to that innovation with a roll-out phase or switch to the next innovation idea. That's why we recommend focusing on a fast return (up to a few months) PoC within a year. What goes beyond this is more basic research or strategic development. In the case of innovations, if we want to rely on best practices, we can expect the following projects - based on our research - in the next 1-3 years:

  • cyber security
  • IoT
  • multi-cloud environments
  • Artificial Intelligence (AI)
  • data processing analytics
  • storage solutions
  • Augmented Reality / Virtual Reality / Mixed Reality (AR / VR / MR)
  • blockchain

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Of these, we would highlight the artificial intelligence (AI) that has been used in machine learning since the 1980s. Now, with the great AI flare up, “only” a multilevel neural network has been opened up to teach and quickly apply (deep learning) with the latest GPUs, that is, we have reached a level of speed and opportunity. We would like to draw attention to an important thing, which is very important for the protection of innovation: we can modify (or discard, if it goes in the wrong direction) the results (experiences) of machine learning and deep learning by further teaching, but it is difficult or impossible to copy or remove parts. In order for us to have a good solution for AI, we need "data philosophers" ("data artists" in other words) who, depending on the industry, understand processes, industrial goals, data, and are willing to think about what the difference between a pen and a brick if we can draw with both on the wall? When the customer's qualitative and quantitative expectations are so high that the state space and the variables cannot be tracked and managed either in the head, in the paper or in the spreadsheet, then the AI, for who this part is a child's play, is to be taught "only" to play correctly.

There is space and opportunity to innovate for every company, be it the basic infrastructure to provide the right work-life coexistence, relieve the employee of the unnecessary administrative burden by online methods or even by monitoring the production and environmental parameters based on more than 100 parameters to decide on the processes in real time. Small companies can innovate just like the biggest companies, in the least cases the best answer is to buy a new production machine. The world seems to drift in the direction that every company is basically an IT company (shoemakers and grocery stores, too), and such a class or a professionally oriented background class gives the company a special taste.

Tags :
IOT CDR Data Process Company Projects Innovation Motivation
  • May 10th, 2019

The essential role of the Local Hero in IoT projects

Where are we heading?

Thanks to the digitization of production, we’re in the midst of a significant transformation regarding the way we manufacture products. This trend points to a rise in automation, as well as the need for product teams to leverage the latest technology more than ever. There’s no doubt about it: We’re entering a new world of industry.

Many organizations might still be in denial about how Industry 4.0 could impact their businesses. Some are struggling to find the talent or knowledge to know how to best adopt it to their unique use cases. However, several others are implementing changes today and they are preparing for a future where smart machines improve their businesses.

What are Industry 4.0 and IoT about?

In spite of this, the greatest challenges companies face when building out their IoT capabilities do not lie in technology. Industry 4.0 and Internet of Things (IoT) are not really only about Machines. In fact, these are about people and transformation. If synergy is achieved with people, we will be just a few feet away from success.

Change management focuses on preparing and supporting the transformation of organisations at the level of individuals, teams and organisations as a whole. Nowadays, many of the change management processes are grafted into models, each with an own strategic approach and inspired by the experience of their developers.

How to get through the transformation smoothly?

In spite of the multiplicity of models, the underlying goal always remains the same: to guide the company and its employees smoothly through the transformation. In order to boost any change management process, there is a secret sauce: the key to success is the appointment of local heroes. These are individuals within a company who respond to changes more quickly than others.

Who will be the local hero?

During an Industry 4.0 change process, we always work in tandem: an external consultant, teamed up with an internal colleague. It is not just a few 'strangers' talking about how employees should do their work, but it is always the impact and influence of a familiar face.

This internal colleague is the so called 'local hero'. This person knows everything about the company, its processes and operations very well. The local hero has influence on events, has his/her own decision-making power and even knows the operational details. In terms of factories, it is typically a middle manager of production, maintenance, engineering or quality assurance who is aware of the goals to be achieved or the problems to be solved. As the result of the collaboration, the local hero has the chance to leave a huge impact on the IoT process.

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„You don’t create the Internet of Things as a stand-alone. Its value will come when it connects to all parts of the organization. That will lead to the transformation of the entire organization.” /Umeshwar Daya/

What is in the focus of innovation? At the centre of the innovation that is unfolding across all geographic, industrial and technological borders are not so much those devices that are being linked together, but the 'connected person.' At the centre, there is a human being who is making use of the applications and services that are enabled by such devices and their unprecedented integration provided in the IoT.

Currently, machines are operated by humans and these machines only passively follow the operators' commands. The main trend of Industry 4.0 will therefore replace this condition by the Prognostics-monitoring system. Production processes will have to allow effective production.

At the same time, they should be flexible due to changing customer demand for particular products. The role of the human factor will be necessary for future manufacturing. The skills and qualifications of the workforce will become the key to the success of a highly innovative factory.

A local hero can make a huge difference here with a future-oriented, human-based attitude where everybody is involved and has its own inner motivation for a bright and common goal. Last but not least, this contribution also has a positive effect on the local hero's career path. What does CDR stand for?

In this context, we can transfer CDR (Cloudera-DELL-REACH I4 reference architecture) abbreviation as a Career Development Revitalisation. A dedicated colleague will have the chance for a life-changing career development.

CDR will most likely offer a sky rocketing career path in the near future for the local hero. Nonetheless, CDR provides unique experience for the local hero, which will make this colleague precious in the eye of the company. Most importantly, however, the local hero can use the acquired knowledge and vision at a higher managerial level.

  • Apr 10th, 2019

Hidden value of diagnostic data

PLC - an alternative solution instead of traditional assembly lines

Among the numerous challenges in Industry 4.0/ Industrial Internet of Things (IIoT) solutions, the most intensely discussed and developed area is probably the pursuit for a continuous, precise and efficient production process. In order to increase Key Performance Indicators (KPI) to the highest possible levels, manufacturers must pay attention to obtaining machine operational data analyzed in a transparent, yet interrelated method.

The abilities of cutting-edge diagnostic tools are increasingly linked to AI/ ML methods. According to Gartner, by 2020, over 40% of tasks related to data analysis will be totally automated. It is no surprise as human data analysis skills are not very agile .

However, some programmable logic control (PLC) vendors already offer machine-fault detecting algorithms through built-in libraries. These solutions help improve production line uptime by providing engineers with machine health assessment. This is a useful tool to prepare preventive maintenance (PVM) before an accidental component failure causes an unexpected system downtime incident.

On the other hand, due to changing customer habits, factories need to be able to respond to personalized product needs. Thus, they should make their production more flexible towards smaller batches than before. Along with this approach, factories will set up innovative and flexible manufacturing areas with interconnected automation islands instead of the traditional fixed assembly lines.

Obtaining elementary machine data
Without any doubt, the greatest bottleneck of IIoT and I4.0 is data transfer between layers that have never communicated with each other before. The number of solutions here corresponds to the number of vendors. Here, a common goal is to substitute classical 'package-sending' and find real-time data extraction methods from the memory area of controllers without any radical intervention.

Industrial robots mostly use Real-Time Data Exchange (RTDE), RESTful API's that leverage the HTTP protocol; and the messages are composed of XHTML and JSON, etc. The situation is not any better with PLC's, where you can find MC protocol, S7, Modbus, Ethernet/IP FINS/TCP, OPC UA, MQTT and so on.

To handle communication challenges between different kinds of systems, machines and devices, we need to create standardized protocols, interfaces and data-interchange formats. Moreover, different devices can serve different sample rates (0.2…500 Hz). This results in different data volumes and densities with different timestamps. REACH's event-based smart data lake is specialized for the fast interpretation of such disorderly data streams.

Motion diagnostics
Since the third industrial revolution, the market of industrial robots has been growing in an unstoppable way. Today, as a result of this enormous growth, industrial robots  including their diagnostics  are in the absolute focus of attention. Robotic arms are mostly preferred because of their manipulative abilities. They are able to move their tools quickly and accurately. This works even with high payload and in a variety of applications.

Special AC servo motors are responsible for moving the individual joints to the correct position. Monitoring their status and diagnosing motion patterns can provide elementary information on their proper functioning.

Under normal operating conditions, motors in a robotic arm work within a current value (disturbance) interval pre-defined by the manufacturer (depending on current inertia). They return to their positions within a given pulse coder error. The motion torque applied to an arm must be cyclical.

With the help of modern IIoT connectors, we can observe cycle patterns by collecting the mechanical parameters of robotic arms. Inappropriate or missed maintenance, collision and mechanical abnormalities can be detected through discrepancies in these patterns. Restrictions to factory limit values or a machine learning algorithm may be appropriate.

For example, with a properly configured notification management system, an overcurrent (OVC) alarm and downtime can be prevented. An escalation process is triggered by the first signs of an anomaly. At the same time, the maintenance staff is immediately informed of the exact technical details and the list of components required for intervention, as well as of the estimated remaining availability.

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Error rates and heatmap
Statistical and predictive tools are based on historical smart data lakes and they cover a wide range of mechanical diagnostics. Time and quantity diagnostics of each error event bring the sequential phenomena into focus. When investigating the advantages and disadvantages of sequential phenomena analysis, we can conclude that it can be served with a relatively small hardware resource requirement for a properly designed database of its computing layers. However, it does not handle or highlight parallel events. This is helped by a so-called correlation heat map, which is a tool for investigating correlations between data series.

The outcome of the analysis is greatly influenced by the quantity and quality of the data. If all data sources from the manufacturing process are integrated into the architecture, the complex phenomena can be examined and solved on a unique analytical interface. This learns the relationship between the tool status and the number of scraps or stoppage causes of the machine, as well as material or operator failure, or even tool failure with the available algorithms.

Key to the correlation heat map below: values close to 1 (red) have a strong positive correlation (time-time movement), while values close to -1 (blue) show a strong negative correlation (time-opposing change).

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The above approaches have both direct and indirect feedbacks to the production process. In both cases, a high resolution interface with the existing MES is essential. To ensure continuous production, the proper scheduling of necessary maintenance is a simple, yet important formula to recycle machine diagnostic data. In case of small series production, we can override any previous production schedule based on the diagnostic data obtained. In case of a predicted failure, we can save precious downtime by optimizing the sequence of series by reorganizing the production between the islands.

All these can be created in an autonomous environment, with machine-to-machine (M2M) communication via standard channels (e.g. OPC UA), without human interaction.

  • Mar 10th, 2019

Application layer protocols

Key aspects of Internet of Things (IoT) and Industry 4.0 are sending and receiving data. To handle communication between different kinds of systems, sensors and devices, we need to have standardized protocols. These protocols should be a little bit different from the currently used protocols over the internet.

Another great example is one from the application side. Consider the classic HTTP/HTTPs based data access and try to cover a modern real-time application with simple HTTP requests, this is not an option. That is where we need to use new type of protocols.

In our current blog posts we will introduce the new protocol definitions for the TCP/IP definition application layer.

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REST - Representational State Transfer
Representational State Transfer is an architectural style that build on certain principles using the current web fundamentals. It generally runs on HTTP. It makes a stateless transfer. REST ignores the details of component implementation and protocol syntax in order to focus on the roles of components, the constraints upon their interaction with other components, and their interpretation of significant data elements. REST has been applied to describe desired web architecture, to identify existing problems, to compare alternative solutions, and to ensure that protocol extensions would not violate the core constraints that make the Web successful. Fielding used REST to design HTTP 1.1 and Uniform Resource Identifiers (URI).

The REST architectural style is also applied to the development of web services as an alternative to other distributed communication types such like SOAP. REST is often used in mobile applications, social networking Web sites, mashup tools and automated business processes.

There are 5 basic fundamentals of REST services which are created for the Web.
  • Everything is a Resource.
  • Every Resource is Identified by a Unique Identifier.
  • Use Simple and Uniform Interfaces
  • Communication is Done by Representation.
  • Every Request is Stateless.

WS - Websocket
The HTML5 WebSockets specification defines an API that enables web pages to use the WebSockets protocol for two-way communication with a remote host. It introduces the WebSocket interface and defines a full-duplex communication channel that operates through a single socket over the Web. HTML5 WebSockets provide an enormous reduction in unnecessary network traffic and latency compared to the unscalable polling and long-polling solutions that were used to simulate a full-duplex connection by maintaining two connections. The WebSocket protocol was designed to work well with the existing Web infrastructure. As part of this design principle, the protocol specification defines that the WebSocket connection starts its life as an HTTP connection, guaranteeing full backwards compatibility with the pre-WebSocket world. The protocol switch from HTTP to WebSocket is referred to as a the WebSocket handshake.

MQTT - Message Queuing Telemetry Transport
MQTT is a machine-to-machine (M2M)/"Internet of Things" connectivity protocol. It was designed as an extremely lightweight publish/subscribe messaging transport. It is useful for connections with remote locations where a small code footprint is required and/or network bandwidth is at a premium. For example, it has been used in sensors communicating to a broker via satellite link, over occasional dial-up connections with healthcare providers, and in a range of home automation and small device scenarios.

COAP - Constrained Application Protocol
The Constrained Application Protocol (CoAP) is a specialized web transfer protocol for use with constrained nodes and constrained networks in the Internet of Things. The protocol is designed for machine-to-machine (M2M) applications such as smart energy and building automation. Like HTTP, CoAP is based on the wildly successful REST model: Servers make resources available under a URL, and clients access these resources using methods such as GET, PUT, POST, and DELETE. CoAP was developed as an Internet Standards Document, RFC 7252. The protocol has been designed to last for decades. Difficult issues such as congestion control have not been swept under the rug, but have been addressed using the state of the art.

AMQP - Advanced Message Queuing Protocol
The Advanced Message Queuing Protocol (AMQP) is an open standard for passing business messages between applications or organizations. It connects systems, feeds business processes with the information they need and reliably transmits onward the instructions that achieve their goals. The capable, commoditized, multi-vendor communications ecosystem which AMQP enables creates opportunities for commerce and innovation which can transform the way business is done on the Internet, and in the Cloud. AMQP is divided up into separate layers. At the lowest level we define an efficient binary peer-to-peer protocol for transporting messages between two processes over a network. Secondly we define an abstract message format, with concrete standard encoding. Every compliant AMQP process is able to send and receive messages in this standard encoding.

In REACH we build our system based on above mentioned technologies, so our approach is to use existing and well-designed open standards and protocols so we support all the application level protocols described above on the top of our modern microservice architecture.


  • Feb 10th, 2019

Advanced document processing by Big Data techniques

To build up a professional document management system is crucial for every organization. It usually provides functions like document storage, document classification, access control, and collaboration. Nice, but is it enough? Can we really use the information stored in these files effectively? In this post, we are going to show how you can gather and use valuable information from unstructured documents by Big Data tools and techniques.

Most of the companies deal with a large amount of unstructured data in various file formats. The most popular types are the different versions of Word, Excel and PDF. In addition, scanned documents and other images are also remarkable. The unified process of these could be a great challenge due to the diverse file types. It is good news that 'processing various data' is one of the main definitions of Big Data (besides 'large volume' or 'fast velocity').

So we have powerful Big Data tools to apply. We can analyze the metadata of documents, get the content in a unified text format  even from scanned documents  or build up a 'google-like' internal search engine. To develop a custom Big Data application with the previously detailed features, we can use plenty of open source software components. Let’s see a Content Extractor and an OCR solution in details.

Apache Tika
The Apache Tika toolkit detects and extracts metadata and texts from over a thousand different file types (such as PPT, XLS and PDF). All of these documents can be passed through a single interface. This makes Tika useful for search engine indexing, content analysis, translation, and much more. With Apache Tika, we can grab all metadata and text-based content from any popular document type.

Tesseract OCR
Tesseract is an Optical Character Recognition (OCR) engine with support for unicode and the ability to recognize more than 100 languages out of the box. This software  released under the Apache License  is free, and its development has been sponsored by Google since 2006. We can effectively use it to extract text content from scanned documents or any other images. Nowadays, Tesseract is considered as the best open-source OCR engine, regarding the accuracy of the recognized texts.

Metadata analysis
Besides their effective content, the mentioned documents also contain a lot of metadata. The most common metadata are: author, creation date, last modification date, last modifier, creator tool, language, content type ..... etc. In case of images, we have metadata about the application that optionally modified the original photo and perhaps the exact GPS coordinates of the location, as well. If we can extract these data and store them in a unified way in a database, we will have the capability to run advanced search queries on them. On top of that, we can also create analytics or visualization about our documents (for example, the distribution of 'Creator tools', a number of documents created or modified in a certain period ..... etc).

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Build a search engine
It is obvious that the more business documents we store, the harder it is to find the relevant information we are looking for. In this situation, a custom internal search engine could be a very useful tool for the whole organization. To build up a search engine, first we have to process all documents we have, and then grab their content, index and store it in a special database, optimized for quick full-text search queries. In case of scanned documents, it’s needed to apply Optical Character Recognition (OCR) to convert the scanned image in an interpretable text format. After the initial document process, we have to build an automated data pipeline, which will ensure the processing of new or modified documents continuously. During the data process, we can also define keywords, and then tag the given document with the found keywords in their content.

Interactive maintenance guide
In a factory with many production lines, regular maintenance is a general task. However, maintenance manuals are usually not unified. It is sometimes hard to find the relevant documentation for a given machine or part. Furthermore, manuals could be updated regularly so it is important to use the appropriate version.

To support this task, we can build a common interactive maintenance guide for all operating machines in the factory. This guide could provide step-by-step maintenance instructions for every machine and store the previous versions of the documents. In order to implement a system like this, we have to process all available maintenance manuals, find the relevant parts in the document, and load the document into a unified database. The use of this continuously updated database and a well-designed user interface will make the execution of maintenance tasks more effective with fewer faults.

  • Jan 10th, 2019

Project methodologies for Industry 4.0

We can see that many factories struggle with implementing successful projects aligned with an Industry 4.0 initiative. Success doesn’t always mean financial return directly. However, it can bring better worker and customer satisfaction, environmental benefits and more: being lucrative in the long term. But anyway, there must be a gain from any project, which in turn will drive your company along the (hopefully long and fruitful) Industry 4.0 journey.

We have seen several good initiatives literally die because of one very important aspect: the lack of a reasonable use case, which would increase the appetite to use big data solutions in the factory. During the last couple of years, we have gained experience at customers and international hackathons. As a result, we have a well-working set of methodologies that helps create value at almost any company.

Not sure if there is value in your use case? Do data pre-evaluation

Many times, companies over-plan the implementation of use cases instead of experimenting with what they have and iterate it until they have the final solution. Are you planning to build an expensive data pipeline for a predictive maintenance system? You had better check the quality of the data and take the first steps with an offline system before building something large.

This is the situation our so-called 'data pre-evaluation' methodology is developed for. In only 10 man-days (optimally lasting about 4 weeks, depending on your availability), you will be able to decide whether to invest in a real-time and precisely built version of the same idea.

reach i4 industry 4.0 projects proof-of-concepts crisp-dm First, prepare and get to know the data by using standard data mining and analysis tools. In one case, our customer had frequent breakdowns on their machining equipment, and process data was present in CSV files. Initially, we cleaned and investigated the data. Then, we concluded that it would be very likely to build a predictive application by using that data as a training set and calculating the predictions on the live stream data. (Of course, the re-training of the system can happen at any time after the initial training.) In later phases of the idea implementation (see the proof-of-concept later in this blog post), the predictive application helped reduce downtime significantly.

If engineers and decision-makers understand how powerful a potential application could be (based on its pre-evaluation), they will likely want to build a Proof-of-Concept, which is usually the next step in the iterative process.

Ready to make the next step? Go for the PoC (Proof-of-Concept)

A PoC is meant to be something its name refers to: proving that the initial assumption or concept was right and the solution satisfies the initial claims. That is, the product will meet customer demands; the solution will bring the desired benefits and so on.

We strongly believe that a PoC needs to contain at least 3 use cases (of low-hanging fruit type). This increases the chance that out of these three, at least one will generate a financial return within one year. This is again very important to convince decision-makers inside the factory.

reach i4 industry 4.0 projects proof-of-concepts Our agile PoC methodology takes about 3 months, and it has three main phases. In the first iteration phase (3 weeks), we explore the use case more deeply than in the 'data pre-evaluation' methodology as we need to build the foundation for a future production environment. It is followed by 3-4 iterations of modelling and development. This is where data and models are prepared, data pipeline(s) are built, and all of these get evaluated.

At the end, it will take at least 1 week and several discussions with our customers to understand the business insights that we have gained and to evaluate the current and future value of the project. Finally, we report the results to the management and discuss the next steps.

Industry 4.0 roadmap – designing strategies

We have been through numerous project implementations with the players of the manufacturing industry. Sooner or later, every company will realize that either a complete Industry 4.0 or a Smart Factory roadmap needs to be developed. Another solution is when an existing strategy is to be updated or fine-tuned. We can help you identify key areas in the factory where proven Big Data technology can help make production more efficient and profitable and help design the strategy that will result in a Smart Factory one day.

Sometimes you need to spice up the idea

We have learnt a lot during and from Industry 4.0 hackathons. One of the most important take-aways is that a spectacular demonstration of the use case is necessary to convince decision-makers to start a project, which will be deployed in a production environment later. Using the 'hackathon method' helped us convince key people to continue and profit from the Industry 4.0 roadmap. For example, the spectacular demonstration of a prototype version of the machining use case was enough to prove that it is worth taking the next step towards the system in production mode.

In sum, if you feel that you need to start building an extensive solution or application for a specific problem, you had better run a small evaluation with the data and information you already have (if you are playful, you can even use the hackathon format). And if you need help, we will be glad to help you with our proven methodologies. Get in touch!

  • Dec 10th, 2018

Digital Transformation 4.0

Digital Transformation shows how companies can upgrade their operations with technology. People like using this term as something 'revolutionary'  a brand-new approach. However, digital transformation is not a new concept. In fact, we are faced with the fourth wave of digital transformation. This applies particularly to the industrial sector, where the digitalization has a long history. Let's look at it briefly.

Digital Transformation 1.0 (1970s): Initial Digitalization.

The whole story began somewhere around the PLCs at the end of the ‘60s. PLC means 'programmable logic controller', which is an industrial digital computer. It has been adapted for the control of manufacturing processes, such as assembly lines or any activity that requires high reliability control, ease of programming and process fault diagnosis. A PLC is an example of a real-time system since output results must be produced in response to input conditions within a limited time, otherwise unintended operation will happen.
Richard Morley is generally known as the 'Father of the PLC', but General Motors delivered the first batch of PLCs in November 1969.

Digital Transformation 2.0 (1980s): Transformation to Paperless Procedures.

In the initial wave, the number of programmed solutions was steadily growing. In the 1960s and 1970s, the development of electronic data interchange systems paved the way for the second wave of digital transformation.
In this phase, computer systems already supported different business activities (e.g. booking, invoicing, ordering and accounting). They enabled different planning and management processes, as well as the coordination of interdependent activities, which are paperless interactions even with external partners. However, to tell you the truth, during the 1980s, paperless office simply meant that all forms of paper (documentation) should be converted to digital format.

Digital Transformation 3.0 (1990s-2000s): Transformation to Automated Procedures.

The trend of using information technology continued during the middle and late 1990s. In particular, automatic identification and positioning technologies were introduced in the mid-1990s to improve the efficiency and safety of operations. The major change was the collection methods, like the adoption of new handling technologies equipped, for example, with sensors.
The automation of certain processes often required the complete redesign of organizational structures, policies and business process activities, as well as efficient information management (at that time, this was called BPR – Business Process Reengineering. Probably, most of you remember it.)
The limitations of static information were still experienced, but higher visibility and different forms of decision support  based on accurate data  were becoming increasingly important to enhance responsiveness during the operations.

Digital Transformation 4.0 (2010s-): Transformation to Smart Procedures.

The basic idea is to integrate different systems and data silos into one central platform. Based on real-time data, this allows decision-making and ongoing interaction with stakeholders thus being actively involved in the manufacturing activities. Data silos occur for different reasons. The earlier digital transformation waves produced tons of different IT applications. Once a company is large enough, people naturally begin to split into specialized teams in order to streamline work processes and take advantage of particular skill sets.

In the new era, lots of data are still processed in isolated systems. However, in parallel, these are immediately transferred to a central information system in order to explore, analyze and distribute relevant and valuable information over different channels to various targets (humans, systems, machines).

Maybe, the most important part of this phase is the 'rise of artificial intelligence'. Artificial intelligence helps you get the work done faster and with accurate results. A central and intelligent information system will facilitate integration and provide the necessary resources to fulfil the required business agility in a flexible way.

An integrated operation of different, but particularly collaborative systems and devices can be realized in a central solution controlled by AI. This can simultaneously handle past heritage, present urgency and future uncertainty for agile mass customization on the whole value chain. From IT perspective, this needs an IoT platform for integration, as well as for fast and smart processing. That’s what REACH offers. And this is the point where Digital Transformation 4.0 and Industry 4.0 directly meet each other.

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  • Nov 10th, 2018

Data assets at a factory

Special benefits of IoT and Industry 4.0 for manufacturing companies

Earlier we emphasized that the biggest winners of Industry 4.0 will be companies that rapidly find out how to turn their data into real business benefits. In this article, we will show you why an integrated Internet of Things (IoT) platform is the proper solution.

IoT and 'data revolution' do not disrupt manufacturing businesses, unlike in the case of other industries like telco and retail. Manufacturing companies seek the optimization potential in their data and the intelligence that can be provided to it. The aims are to make production more efficient, reduce scrap and waste, in other words: to support lean manufacturing.

Think about today’s technological advancements in manufacturing compared to the production of the Ford Model T. A lot of machines are automated and most of them collect data. Still, fewer than 5% of the machines in factories are monitored in real time. This is a huge obstacle to full transparency.

Estimating the value of data and the information contained in a project is essential to decide where to implement industry 4.0. Let’s see the major approaches that can be used to determine the value of data within an organization.

Different approaches to measure the value of data:
  • Benefit monetization approaches: the value of data is estimated by defining the benefits of particular data products, and then monetizing the benefits.

    Let's see an example: a machine starts producing waste from time to time, with no significant change in the operation state. When this happens, the machine is stopped for 20-30 minutes, and the tools get cleaned. If producing waste can be predicted, the operators are able to avoid the problem. This generates savings of 1-hour machine time each day and about 50 waste pieces per day.

    In this example, we use data from the machine to predict unwanted events, to avoid them by intervention and to create measurable benefits.
  • Impact-based approaches: here, the value is determined by assessing the causal effect of data availability on economic and social outcomes, even within the organization. In addition, (processed) data make daily work more effective and help reduce the frustration of workers fall in this category.

    For example, if repetitive, boring work can be automated, it lets workers and analysts do work with more added values and feel happier.

    There are further approaches, e.g.: cost-based, market-based, income-based, etc. However, the above two are the most applicable to the manufacturing industry.

Go for the business benefits

The key question is: will the particular data provide measurable and tangible business benefits? The critical first step for manufacturers who want to make use of their data for improving yield is to consider how much data the company has at its disposal.

Most companies collect vast troves of process data, but typically use them only for tracking purposes, not as a basis for improving operations. For these players, the challenge is to invest in such systems and skill sets, which will allow them to optimize their use of existing process information.

The data silo problem

Having data is not enough. Very often, companies' data remain under the control and use of distinct departments, and thus the information flow is blocked. In these situations, we talk about data or even information silos.

A data silo is a repository of fixed data that remains under the control of one department and is isolated from the rest of the organization.

Data silos are huge obstacles if a company wants to make operations more visible and transparent. If you notice that data silos have been developed in your organization, you may want to look for a solution and build bridges between them. In this case, you will probably end up with an IoT platform.

The solution – an IoT platform

An IoT platform like REACH is basically the nervous system of any factory. It connects different functional units, machines and sensors with humans, it transfers signals in both ways, stores and analyzes data and it must be able to exhibit intelligence to some extent.
reach i4 data iot fog computing nervous system central system  industry digital twin Data must be flowing from different production phases, machines and departments with no friction. Predictive maintenance algorithms need to monitor the whole procedure for the timely alert of the right staff that can prevent or eliminate any failure.

If you want to reach this functionality, a cross-department/ cross-operational IoT platform must be in operation at your company. This is the preliminary condition of any integrated Machine Learning and Predictive Maintenance solution .

Sometimes, only one missing link between data sources can provide a huge benefit. Imagine that you operate a gluing machine at some point in an assembly process. The adhesion force of the glue varies with time and you don’t know why. There are days when you produce 20% scrap because of insufficient adhesion quality.

Even if you analyze the data collected from a machine, there is no pattern that would imply causality between machine data and the final product quality. At some point, you get the idea of joining the machine data with the factory weather station, and you will find that the gluing quality correlates with air humidity, so you can start solving the problem and reducing scrap significantly.

Implementing a profitable use case

Implementing a use case that provides measurable business benefits is not solely dependent on the data sources and the quality of the data. The ability of creating real value must lie within the organization and require good methodologies.

In our next blog post, we will explain some of these proven techniques that help companies implement successful and profitable use cases and projects.

  • Oct 10th, 2018

Data security

A key factor for applications dealing with lots of data – including complex event processing – is security. Nowadays as Internet of Things (IoT) is more popular than ever, one can hear more and more stories about security breaches. Simple internet connected devices are often less secured, and thus more vulnerable to different types of attacks.

Limitations of Fog Computing

REACH uses Fog Computing , which means that none of the data leaves the factory's territory, thus making any external attack impossible. Naturally, this doesn't mean that everything is secured. If hundreds, thousands, or even tens of thousands of employees and vendors can access every data without restrictions, the chance of a potential disaster is excessive. Just think about what could happen if someone deletes all data collected in the past years  it doesn't matter if it is intentional or not.

Prevention is better than cure

Most companies only think about security after Armageddon has already happened: a leak or destruction of private data. All such incidents are avoidable with enough care. Fortunately, there are multiple ways to address these problems, and REACH also has these solutions integrated by default.

How can Kerberos help authentication?

Kerberos – developed by MIT – plays a key role in authentication. It only lets people (and services) access data if they can prove their identity. The client authenticates itself at the Kerberos server, and receives an encrypted timestamped ticket-granting ticket (or TGT for short). Whenever it wants to access a new service in the TGT’s lifespan, it asks for a separate ticket for the specific service. Different services are accessible only with these valid tickets, which also have a lifespan, and thus they are unusable after a short period: all tickets are encrypted with AES256, which could take an eternity to brute force with billions of supercomputers.

Improving security by LDAP (Lightweight Directory Access Protocol)

The next level of security is authorization, where rules specify who can do what. Lightweight Directory Access Protocol – shortened as LDAP – is an industry standard for distributed directory access, created by the University of Michigan. Its OpenLDAP implementation is fully open source and integrates well with Kerberos, thus making it a perfect fit for security. It holds all information about users and services, and says which user has permission to access a specific resource.

Preventing data loss by TLS (Transport Layer Security)

However, one piece is still missing: what if fog devices are communicating with each other? They still have to send data across the local network to collaborate, and one could sniff those packets. The solution to this is using Transport Layer Security (TLS), which is a cryptographic protocol. It encrypts the data over the network, and as a result, only the intended recipient can open the messages.

Remember, no matter how tall, spiky or strong the fence is what you have at 95% of your territory’s circumference! Your fence is as strong as its weakest part.

None of the above technologies would be enough alone, but together they form an all-round security layer to protect your valuable data.

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  • Sep 10th, 2018

The role of Industrial IoT in maintenance and manufacturing optimization

Maintenance is a daily task in factories to keep machines healthy and the whole manufacturing process efficient. The main goal is to do maintenance before a particular machine starts producing waste or even suffers complete failure. It is easy to prove that preventing machines from being stuck means lower operating costs. In addition, it helps keep production smooth and fluent. Still, a lot of factories have a hard time dealing with downtime due to asset failure.

Standard maintenance procedures – Preventive Maintenance (PM)

The purpose of regular care and service done by the maintenance personnel is to make sure that the equipment remains productive, without any major breakdowns. For this purpose, maintenance periods are specified conservatively. These are usually based on data measured by the equipment manufacturer or at the beginning of the operation. However, such procedures do not account for the actual condition of a machine. This can be influenced by different environmental effects – like ambient temperature and air humidity –, raw material quality, load profiles, and so on.

In order to take the ever changing operation conditions into account, condition-relevant data needs to be collected and processed. This is condition based maintenance (CBM). In case of machining it is essential to measure ambient parameters, machine vibration, sound and motor current, which give a picture about the concrete health state of the machine and machining tools. The availability of this data enables making the step towards a more sophisticated maintenance mode: Predictive maintenance.

In order to take the ever-changing operation conditions into account, condition-dependent data need to be collected and processed. This is called Condition-Based Maintenance (CBM). In case of machining, it is essential to measure ambient parameters, machine vibration, sound and motor current, which give a picture about the health state of the specific machine and machining tools. The availability of these data enables you to make a step towards a more sophisticated maintenance mode ̶ Predictive Maintenance.

How to bring maintenance to the next level: Predictive Maintenance (PdM)

Maintenance work that is based on prediction, presumes fulfilling the following requirements. First of all, data collected from the asset should contain the information that shows the signs of an upcoming event. In other words, patterns precisely describing each event should be identifiable in the measurement signals. If this hypothesis holds true, the next step is either to hard-code the conditions indicating any oncoming failure, or to use a machine learning algorithm to identify and literally learn the particular failure mode patterns.

The second requirement comes into picture as soon as the patterns have been identified and the model is capable of predicting an unwanted event soon enough to take action. This requirement addresses the architecture of the system by making the following prediction: “It needs to operate in real time.” The reason is that there are many applications where damage can be predicted only shortly before the event (usually measured in minutes or seconds). Advanced IIoT systems feature real-time operation.

And last but not least, events and data are very complex in manufacturing situations where multiple machines and robots are involved. It means that a group of machines together has an impact on the product quality or operational efficiency of consequential machines. Processing such data requires a system with high computing performance and tools to handle complexity.

The complex data processing and predictive capability of REACH lies in the most advanced Big Data technologies, built-in Machine Learning algorithms and the real-time Fog Computing architecture . The system is capable of learning and distinguishing between failure modes and sending alerts in case of an expected breakdown.
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Although Predictive maintenance is a big step for most manufacturers, there is still a next level to go for.

State-of-the-art: Prescriptive maintenance (RxM)

Prescriptive Maintenance requires even more detailed data and a checklist on what actions to take in case of a detected failure mode pattern. Although technology makes implementing prescriptive systems utterly possible, few organizations make it to this point. This step requires very good harmonization between the maintenance and the production departments, a fair understanding of the problem and efficient cross-departmental information sharing. These are the key criteria of a successful Industrial IoT implementation anyway.

Manufacturing companies that make the effort to collect data, analyse problems, understand and prepare their data, as well as to identify the patterns of inadequate operation, can reach the level of a Smart Factory including maintenance operations. In the presented case, not only the checklist is being displayed on REACH UI, but emails and SMS can also be sent to the maintenance personnel and other relevant stakeholders. This minimizes the time required to prepare for taking necessary actions.
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Besides using email and SMS alerts, REACH can send status messages to engineers and other personnel even via our chatbot called RITA. Using state-of-the art technology can be fun, too!

  • Aug 10th, 2018

IoT Gateways

In our earlier posts, we talked about how to store, process and analyze data. However, we missed a crucial step: How to collect data? For the IoT, an outstanding challenge lies in connecting devices, endpoints and sensors in a cost-effective and secure way to capture, analyze and effectively gain insights from massive amounts of data. An IoT gateway is the key element in this process. Below, we are going to describe why.

The definition of an IoT gateway has changed over time with the developing of the market. IoT gateways function like bridges – just like traditional gateways in networks. Indeed, they bridge a lot by being positioned between edge systems and our REACH solution.

IoT Gateway market

IoT gateways fulfil several roles in IoT projects. IoT gateways are built on chipsets that feature low-power connectivity and may be rugged for critical conditions. Some gateways also focus on fog computing applications, in which customers need critical data so that machines can make split-second decisions. Based on this, IoT vendors can be divided into three groups. Suppliers who only provide hardware (Dell) belong to the first group, companies who focus on software & analytics (Kura, Kepware) are in the second one, while the third group contains end-to-end providers (Eurotech).

Our IoT Gateway solution belongs to the software & analytics group, which is an OPC client (communicating with an OPC server). OPC is a software interface standard that allows the secure and reliable exchange of data with industrial hardware devices. reach i4 opc server  iot gateway mqtt kafka

What are IoT Gateways?

Gateways are emerging as a key element of bringing legacy and next-gen devices to the Internet of Things (IoT). Modern IoT gateways also play an increasingly important role in helping to provide analytics. Thus, only the most important information and alerts are sent up to the REACH to be acted upon. They integrate protocols for networking, help with the management of storage and analytics on data, and facilitate secure data flow between edge devices and REACH.

Mainly in Industrial IoT, there is an increasing movement towards the fog as is the case with many technologies

Intelligent IoT gateways

With fog computing (and the movement to the edge overall), we really enter the space of what is now known as an intelligent IoT gateway. In the initial and more simple picture, an IoT gateway sat between the sensors, devices and so forth on the one hand and the cloud on the other. Now, a lot of analytics and filtering of information is increasingly done closer to the sensors through fog nodes for a myriad of possible reasons as explained in our article on fog computing. The illustration below shows where the intelligent IoT gateway (and soon all of these will be intelligent) sits in an IoT architecture.

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(img source: )

  • July 10th, 2018

Machine Learning

Machine Learning is a process of making software algorithms learn from huge amounts of data . This term was originally used by Arthur L. Samuel, who described it as “… programming of a digital computer to behave in a way, which is done by human beings …”.

ML is an alternative way to build AI with the help of statistics. The aim is to find patterns in data instead of using explicitly hard-coded routines with millions of lines of code. There is a group of algorithms that allows you to build such applications, which can receive input data. In addition, these can predict an output depending on the inpu t.

ML also can be understood as a process, when you 'show' tons of data – texts, pictures, sensor data – to the machine with the required output. This is the training part. Then you 'show' a new picture without the required output, and ask the machine to guess the result.

Use cases

Machine learning has grown to be a very powerful tool for solving various problems from different areas, including:

  • text processing – for categorizing documents or speech recognizers (chatbots);
  • image processing – for training an algorithm with hundreds of thousands of tagged pictures to be able to recognize persons, objects, etc.

However, from our point of view, the more important use-cases are those where factory machines and processes are involved. In this case, we have to collect, assort and store many different sensor readings from various manufacturing robots. We intend to train our machine learning solutions for different purposes like the prediction of machine failures.

Before the training, different tasks have to be done:
  1. data preparation, for example, filling or throwing out empty cells, data standardization, sorting important features etc;
  2. training, which involves feeding the cleaned data to the algorithm in order to be able to adjust itself;
  3. measuring our solution and improving it with new data and parameters.

For these purposes, a data pipeline should be built using the same processes for the newly arrived data as in the training state. With REACH, we are able to do each of these tasks  data preparation, training and building the data pipeline  easily and in a user-friendly way through the UI. Thus, we will have useful solutions, which will result in downtime and cost reduction, as well.

Machine Learning within REACH

We offer solutions for many different problems occurring during the lifetime of a machine learning project. We have different tools for different roles. We provide an easy and simple graphical UI with pre-configured models, which helps you focus only on the data and the pattern behind it.

Of course, with this approach, you will also be able to tune the model parameters and compare them to find out which parameters are more suitable for your application.

REACH is also perfectly suitable for developers who want to build their own solutions.

With an embedded Jupyter Notebook, users are able to build any model with different technologies including scikit-learn, Spark ML lib, TensorFlow, etc. These models could be deployed to a single machine or distributed to the cluster to reach the best performance and scalability.

Towards the future

Machine learning as a term is really pervasive today. However, it is often used in the wrong way, or it is mixed up with AI. or with deep learning – our following blog post topics. With this introduction, you could get a little insight into this technology. Our aim was to provide you with a general picture on how to build applications that are able to improve their performance - without any human interaction - using data analysis and the feedback of performance.

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  • June 10th, 2018

Hybrid Architecture

Modern data lakes or classic storage layers?

As we described in our previous blog post, Data Lake for Big Data is a required technology to cover all needs of Industry 4.0. But what about your classic data? Should you transfer them to a data lake? Do you have to redesign all your applications and processes to use a new storage layer?
The answer is definitely not. You do not need to discard your classic databases, systems and processes that use them. According to our terminology and best practices, a classic database engine can operate in smooth symbiosis with the modern Big Data Lake System approach. This only requires a well-designed architecture. However, this is not easy to create.

How can you combine different datasets?

Our experts designed REACH to be ready to handle such situations. We call it a hybrid architecture, where each data goes to its proper place. Some data should be stored in the data lake system, but some of them should go to the classic storage layer.
REACH is designed to be ready to combine different data sources both on process and analytical levels. Thus, at the end of an analysis, you will not be able to distinguish whether a particular data came from the classic layer or from the Big Data Lake.
We firmly believe in using the proper storage technique. According to our approach, each data should go to its proper storage layer. Thus, we cannot advise you to use only one type of storage for all your data.

Further advantages of REACH

Going further into the implementation of this technology, our experts designed REACH with the aim of handling multiple storage layers. However, this implies not only choosing between the Big Data layer and the classic layer.
We suggest that the storage techniques used inside the data lake should be different from the ones in the classic storage layer. A good example for this is when Kudu, HBase and HDFS operate next to each other. This results in extending the storage techniques from the classic layer. Here, the techniques of relational database storage and standard file storage are mixed. This is why we cannot say that one database engine is sufficient.
REACH was designed to support the above multi-storage approach with the aim of getting the maximum out of your data.

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  • May 10th, 2018

Data Lake

In the world of Big Data, traditional data warehouses are not sufficient anymore. These are not enough to support the requirements of Industry 4.0 technology level or to become the foundation of true real-time solutions.
While traditional data warehouses can only offer structured data storage, Data Lake can provide a solution for maintaining the original format and state of the data. Moreover, it can also provide real-time access to them.
The greatest advantage of Data Lake is that it is capable of storing a tremendous amount of data while preserving the raw format in a distributed, scalable storage system. Therefore, Data Lake can store data coming from various data sources, and thus it is adaptable for future requirements. The result is such flexibility, which cannot be provided by the current data warehouses.

What can a Data Lake offer?

The concept of Data Lake enables factories to fulfil the requirements of Industry 4.0. It also makes data generated during production accessible for other participants in the production line. In addition, this can be achieved in the swiftest, smoothest way by the Complex Event Processing method, which was introduced in our previous blog post. Data is stored in its original raw format, and thus there is no data transformation that would slow down this process. For this reason, REACH has put Data Lake in the heart of its architecture. In this way, we contribute to the competitiveness of our partners. Without the modernization of the storage process, neither real-time analysis nor automations would be possible.

Cut your costs by Data Lake!

Companies that seek to utilize machine learning methods need to possess a wide range of data sources to provide sufficient amounts of data for the algorithms. Cost is also an important element. In case of a Hadoop-based Data Lake  which utilizes well-known big data techniques  storage costs are minimal compared to a standard data warehouse solution.
This is because Hadoop consists of open source technologies. Furthermore, due to the distributed setup, its hardware requirement is also lower, so it can be built even on commodity hardware.

Should data warehouses be replaced?

During the design of REACH architecture, integrability was one of the main aspects. Therefore, REACH offers interfaces for connecting to various data sources and applications. See our upcoming blog post of hybrid architectures!

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  • April 10th, 2018

Complex event processing

Complex Event Processing paradigm (CEP) is a fundamental paradigm for a software system in order to self-adapt to environmental changes. It was introduced to follow, analyze and react to any incoming events requiring near real-time responses by early detection and reactions to emerging scenarios.
A CEP architecture has to handle data from multiple, heterogeneous sources applying complex business rules, and driving outbound actions. The use of techniques for tracking, analyzing, and processing data as an event happens. It is useful for Big Data because it is intended to manage data 'on-the-fly'.

The amount and complexity of data is growing

CEP utilizes data generated continuously  anywhere in a factory  from different sources such as sensors, PLCs, location tracking data, AOI, etc. These data are generally different from former data sources, as they are not prepared or clarified in any way. Therefore, such data tend to be messy, intermittent, unstructured and dynamic.
There is a need to handle data asynchronously, which means that the architecture should facilitate multiple processes simultaneously on a single message or trigger. The number of devices, the volume and variety of sources, as well as the frequency of data are all growing. Thus, they cannot be scaled sufficiently by using traditional approaches for computing, storing and transporting them.

Complex events in real time

In several use cases, latency matters. Delays between a data event and a reaction often must be near real-time. Throughput is impacted along with data growth, and delays will become unacceptable. Latency, that is, a delay between the time when an event arrives and when it is processed  must be low. It should be typically less than a few milliseconds, and sometimes even less than one millisecond.

Possible dangers of traditional approaches

Traditional approaches to centralizing all data and running analytics (even in the cloud) are unsustainable for real-time use cases. Traditional and cloud-based data management and analytics can pose security challenges as they are physically outside the data centre's security perimeter. As machine learning intelligence has become more commonplace in devices out in the field, those devices are becoming more complex. They require greater CPU and memory, as well as drawing more power. The increasing complexity slows down processing and leads to data results being discarded. It is because by the time the results from the device are gathered, more recent data is desired.

Walking the bridge towards complex event processing

There is a need to bridge the gap between the traditional approach and solutions with new Big Data technologies, such as CEP. Leaders across all industries are searching ways to extract real-time insights for their massive data resources, as well as to act at the right time. Bridging this gap will enable your company to become a real Industry 4.0-ready factory. Your business outcomes can be maximized by making better-informed, more automated decisions. And thus, you will be able to deliver higher quality products and/or services to your customers. Our REACH (Real-time Event-Based Analytics and Collaboration Hub) provides such a bridge for you. Let’s make a Proof-of-Concept together to reach your low-hanging-fruits and deliver tangible business benefits within a couple of months!

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(img source: Steinberg, A., & Bowman, C., Handbook of Multisensor Data Fusion, CRC Press 2001)

  • March 10th, 2018

EDGE vs FOG vs Cloud

In our previous technical blog post, we revealed the technology behind the term 'Fog Computing'. We described Fog Computing at a very high level as something, which brings cloud functionality closer to the data while the data is transported a bit away from the edge. Where the data crosses the computing functionality; we are talking about Fog Computing. Now it is time to make a comparison between Edge, Fog and Cloud computing.

Edge computing

In edge computing, physical devices, machines and sensors are directly connected to the data processing devices. Data are processed by these devices, especially aggregating, transforming or running nonperformance-intensive algorithms. This technology is usually used when the data is in digitally recognisable format. In this case, edge nodes transform these non-digital values to digital ones, and then transport them into the fog layer for further analysis. It is also possible to do nonperformance-intensive pre-calculations on the edge nodes. However, this is not really preferred because you lose the possibility of running complex algorithms on raw data in the fog layer.

Fog computing

As we discussed in our previous post, fog computing is about the ways of processing complex performance-intensive algorithms on data collected from the edge nodes. These algorithms use more resources and /or require data to perform calculation. These are not available on the edge nodes, so they cannot be run on them. In the fog layer, it is also possible to generate control signals. These can be transported to the edge nodes, and then the system will be ready to control any machine or device based on complex, calculated events.

Cloud computing

Cloud computing is about buying resources from cloud providers and putting data into the cloud for analysis. IoT is unlikely to push all raw data into the cloud system. In the cloud computing layer, you have to distinguish private and public cloud systems. A public cloud does not mean that your data will be available for public access. However, resources and services can be ordered by anyone who pays for them.

Private cloud systems are more about connecting several locations into one centralized architecture  such as, for example, a data centre  and about owning the whole architecture by the same company. Both private and public cloud systems are (external) network dependent. Thus, in Industry 4.0 architecture, it is advised to use them only for aggregated cross-company data analysis.

So as you see, we could not say that Edge, Fog or Cloud computing can cover a complex Industry 4.0 system alone. According to our experience with implementation, a fog computing layer is the most important one in such architecture. In addition, it must live in a perfect harmony with both edge and cloud systems.

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  • February 10th, 2018

Fog Computing.

In this context, „Fog” means that „Cloud” moves down, closer to the ground, to the machines, sensors and legacy systems.

Fog and Cloud computing are complementary to each other. As the amount of data increases, transmitting it all to the cloud can lead to challenges such as high latency, unpredictable bandwidth bottlenecks and distributed coordination of systems and clients.

Fog computing brings computing and applications closer to the data, saving bandwidth on billions of devices and enabling real-time processing and analysis on huge datasets and streams.

Our product, REACH is a Fog computing platform that delivers real-time, event stream processing capabilities by using distributed computing, analytics-driven storage and networking functions that reside closer to the data-producing sources

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