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Q&A: Harnessing Data Can Unlock New Doors in the Race to the IIoT

Feb. 25, 2017
Technology Editor Bill Wong talks with Philipp Wallner, industry manager EMEA at MathWorks, about the Industrial Internet of Things and its associated data challenges.

The Industrial Internet of Things (IIoT) differs from consumer and commercial IoT (see “What’s the Difference Between Consumer and Industrial IoT?”). It has challenges with data and system complexity that are tougher than those associated with other IoT platforms.

I talked with Philipp Wallner, Industry Manager EMEA at MathWorks, about the IIoT and its associated data challenges.

Wong: What is the Industrial Internet of Things and where does data come into play?

Wallner: The industrial world is rapidly changing with the emergence of smart industry. Today’s production machines and handling equipment have become highly integrated mechatronic systems with a significant portion of embedded software. The growing amount of data has become a major driving force for smart industry and has played a pivotal role in the birth of the Industrial Internet of Things.

Vision sensors, electric and hydraulic drives, production machines, and power plants—they all collect measured data during production operation. However, collecting data does not provide any value on its own. It is the information inside the data that has to be extracted in order to gain additional knowledge about product quality, energy consumption, machine health status, and other economically relevant parameters.

Analytical and statistical algorithms for condition monitoring and predictive maintenance can be used to derive actionable insights from data that has been collected and stored in files, databases, or in the cloud. This concept is taken one step further with model-based predictive maintenance, when an observer model is installed that’s capable of deriving states of factors that cannot be directly measured.

The large amount of measured data needed is enabled by powerful sensor hardware, which executes complex algorithms often under harsh conditions and using minimal space. The sensor hardware typically provides preprocessing and then forwards the results to the controller or to another data-collection point. The sensors act together to form a dense network known today as the IIoT.

Wong: What is the role of embedded software in smart industry?

Wallner: As smart industry evolves, software components provide a significant part of the entire added value of machine or production plants. Embedded software running on PLCs, industrial PCs, or FPGAs involves closed-loop control functionality, which ensures product quality as well as predictive maintenance algorithms for increased uptime without service intervention. Furthermore, supervisory logic for—in many cases, even safety critical—state machines, error handling, and automatic generation of optimized movement trajectories are all implemented in embedded software. 

Wong: As the industrial world becomes increasingly technical and complex, does this present challenges for workers?

Wallner: The growing trend to increase the size and complexity of the code on production machinery is a challenge for classically trained machine builders. Many are mechanically focused and need to maintain experience with elaborate workflows and toolchains for mechanical construction. When it comes to software design, machine builders rely on traditional methods for programming and testing on hardware. However, they are often unaware of tools for modeling, simulation, automatic testing, and code generation, which are widely used by their engineering peers in aerospace and automotive industries.

Measured data collected during production is preprocessed and used with mathematical observer models to provide predictive maintenance functionality integrated with the production plant’s IT system. (Courtesy of MathWorks) (Click image to enlarge)

While it may be obvious for serious mechanical engineers to use a CAD tool and run simulations before physically building the mechanical structure of a machine, in the case of embedded software, it is entirely different. A major portion of machine software is still programmed manually and comprehensively tested when the machine is available.

Successful engineering teams understand that in order to keep up with the demands of next-generation systems, they need a more integrated approach. In the case of smart industry, it requires engineers of all three mechatronic domains—mechanical engineering, electrical engineering, and software engineering—to work together concurrently and evolve the way of designing, testing, and verifying machine software to reach the expected level of functionality and quality.

Wong: What has MathWorks been doing to help industrial businesses gain and/or maintain their competitive advantage?

Wallner: Providing sophisticated sensor networks presents one of the essential prerequisites for realizing the efficiency, cost and, therefore, competitive advantages promised by smart industry. To become innovative leaders in their market, equipment manufacturers need to rapidly develop skills and expertise in these new design approaches and technologies.

As mechanical engineers typically are not experts in software engineering, they can increase their productivity and system reliability by using Model-Based Design tools like MATLAB and Simulink. These tools facilitate modular development of automation components, hardware independent testing, and automatic code generation, which can implement algorithms for specific hardware platforms at the touch of a button.

Wong: How is real-time simulation and testing redefining the way the industrial world works?

Wallner: Models enable the intuitive and clear construction from predefined building blocks and continuous verification. With this approach, design flaws are corrected early on, which considerably shortens design cycles. Next, the algorithms need to be implemented, which can be quite challenging using traditional methods.

Real-time functionality is directly generated from simulation models using automatic code generation. The tested algorithm is directly translated into real-time C, C++, IEC 61131-3, VHDL, or Verilog code. Doing so not only saves time, but also enables the creation of innovative solutions in small development teams. Model-Based Design with automatic code generation enables engineers to build a machine or plant without worrying about programming language details.

Wong: What advice can you offer to industrial businesses trying to keep up in the race to the IIoT?

Wallner: Keeping up with, and being a leader in, the worldwide smart industry race requires companies to offer increasingly efficient and cost-effective products, as well as maintain an open mind to the new business opportunities presented by the smart industry and the IIoT.

For example, today’s production equipment has a lifespan of more than 20 years. During this time, these systems are rarely modified to avoid production loss. Being able to design and test new software separately from the machine will enable companies to offer revenue-generating upgrades to their customers in order to expand the capabilities of the machine.

Industrial companies who manage to shift their focus toward interdisciplinary design thinking (rather than production thinking) will emerge from the transformation as leaders in their areas and with new business models for their market.

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