IoT and the digital twin

16th December 2019
Joe Bush

Industry 4.0 or the Industrial Internet of Things (IIoT), describes the application of IoT in industrial processes and manufacturing. By implementing solutions related to big data, machine learning, digital twin, artificial intelligence (AI), predictive analytics etc., manufacturers aim to achieve the optimal level of responsiveness, adaptiveness and fully connected processes. Joe Lomako, Business Development Manager (IoT) at TÜV SÜD explains.

Some of the key concepts are the convergence of cyber physical systems, with the advent of solutions such as data analytics, robotics and wireless communications. This results in the emergence of intelligent and autonomous networks that are capable of communicating and interacting with one another, and centralised control systems that increasingly give rise to decentralised decision making.

By combining the strengths of the physical and virtual worlds, cyber physical systems have the potential to significantly enhance industry performance, facilitate new products and spark innovative business models. In the longer term, advances in autonomy and flexibility will drive a shift in the economic principles of production. For example, it will be possible to manufacture small lot sizes cost-efficiently, meeting an increasing demand for customised products.

Digital transformation 

Advanced new sensors are already finding their way into modern manufacturing lines, facilitating informed decision making, but this is just the beginning. This ongoing digital transformation is driving innovation across a wide range of areas such as agriculture, energy, transportation and healthcare. Industrial manufacturing will face massive disruption as development moves towards fully connected, self-organising intelligent factories.

For example, this will trigger significant potential for efficiency improvements, such as reducing energy consumption and preventing down-time. Furthermore, industries can utilise smart components to improve asset and supply chain management, enhance quality, and shorten time to market.

While in theory technical components can be combined, in reality the devices may operate on differing technologies and protocols. In other words, the components each speak a different language, which can cause significant interoperability issues. The question is, how can we facilitate smooth and dynamic interoperability among these odd components? The solution is an electronic reproduction, a so-called ‘digital twin’ of each physical component.

The digital twin could emulate the combination of all these different components to determine how it would operate; and any issues observed could be mitigated before final completion of the physical system. This ‘digital twin’ is also in use after commissioning of a system or plant.

This is because any manufacturing environment, which is made up of a number of systems that are interconnected, often runs the risk of a part breaking down, which could cause a ripple effect throughout the entire production line. For example, a robot which performs an important serial function. Of course, that breakdown can have serious consequences as production could be slowed, interrupted or even drastically halted. This is turn could mean loss of revenue and in some cases discontinuity of the business.

Optimising efficiency

In addition, manufacturers are continually looking at ways of optimising efficiency to minimise cost and boost profitability. In today’s Industry 4.0 domain, digital twins can be operated in parallel to the ‘real’ factory, where thousands of sensors constantly collect, process locally (edge computing) or send back data for processing on a larger scale. This can be incorporated into the digital twin to constantly work for improvement. While this may seem complicated, the benefits are significant. For example:

  • Constant monitoring can determine if a machine is about to fail, so any potential issue can be mitigated without interrupting function, but it can be modelled on the digital twin to assess the size of any problem.
  • Data monitoring and analysis can be used to make iterative improvements to operations, improving efficiency and reducing costs in real time. For example, a programmed robot which is operated in a specific sequence could be constantly modelled in parallel to the reduce cycle time of that sequence.
  • Probably one of the greatest uses of the digital twin is planning, as an entire factory can be simulated before the first brick is laid.

Of course, challenges to reaping the benefits of deploying a digital twin also exist. For example, there are many technologies vying for a place in the IIoT and as mentioned earlier multiple technologies bring the problem of interoperability. This means that any digital twin may have to cope with processing data from many different protocols. There is also the cost challenge of replacing existing equipment to interoperate and ‘dock’ with the digital twin. As the convergence of enterprise IT and operational technology sees systems and devices exchanging and interpreting shared data, another challenge is cyber security.

As Industry 4.0 uses more complex IT, these systems face a greater number of attack scenarios and have the potential to cause much more severe impact than office automation systems. A connected manufacturing plant now becomes a target for nefarious attacks and if not adequately protected could have disastrous consequences. This means that successful IT security is a major success factor, without risking serious damage to operations, sensitive data, machines or even people and the environment. It is therefore essential to deal with IT security measures at the early planning stage. While, it is clear that the digital twin is changing industry, it is clear that their remains many challenges which must still be resolved.

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