SECTORAL ANALYSIS OF MANUFACTURING (NACE SECTION C), EU-28, 2016
4.3.2. Major Challenge 2: Implementing AI and machine learning to detect anomalies or similarities and to optimise parameterssimilarities and to optimise parameters
There are several machine learning systems provided by major Internet players like Google, Microsoft Azure IBM Watson, Nvidia, Intel, etc. These are using different kinds of implementation from the deep learning or other algorithms. Deep learning usually needs a large amount of carefully selected training data to be accurate. There is a need for time series data handling to detect similarity or anomaly with an easy set up. This is one basic principle that is required to get successful implementation to industry.
As there are not so many data scientists for every company or domain, a solution suitable for normal automation engineers should be developed.
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Due to the lack of mechanistic models (‘digital twins’) in cattle monitoring, deep learning is becoming the major tool for detection and prevention of health and reproduction problems.
Even though we have large libraries using a variety of programming languages, this is not enough since engineers with a common PLC/DCS background cannot use them. This will require software or framework that can be configured and connected easily to system. Existing runtime systems are not even capable of running algorithms fast enough. Again, this will require an edge computing device (perhaps with GPU) to run analysis to provide result in reasonable time.
Another interesting aspect is cognitive services. As some high-end systems will understand speech and run actions or use background services, they are providing new natural language understanding (NLU). One problem with these services is how to integrate them into the production unit without access to Internet.
It will require some security and DMZ set-up to use it in safe way. Or another implementation could be a private hybrid cloud solution. Nevertheless, this new AR/MR application can be a real game changer for user interfaces. Maintenance people can talk and walk and get instant information from the devices and systems nearby.
Condition monitoring techniques can be applied to many types of industrial components and systems, although often at an additional cost. To determine which level of condition monitoring machinery warrants, a criticality index method can be utilised – categorising machinery into critical, essential and general purpose – that takes into account factors such as downtime cost, spares proximity, redundancy, environmental impact and safety. Commonly, the business value required from condition monitoring depends on higher availability of equipment and, for production processes, information provision to be able to plan and act on maintenance proactively instead of reactively, decreased cost and improved on-time delivery. Other business values that may be of interest are safety, and optimal dimensioning/distribution of spare parts and maintenance staff. Thus, serious breakdowns and unplanned stops in production processes can largely be avoided using condition monitoring.
It is possible to combine quantitative approaches and methods (e.g. using machine learning, historical data/
big data) with qualitative approaches and methods to achieve a higher level of prediction accuracy and identify more types of problems/issues. Regarding qualitative approaches and methods, these require a deeper understanding of the equipment or process and the application/area to be able to model the data and find relationships based on sometimes more than 3–5 parameters that together may characterise the issues. Furthermore, (on-line) condition monitoring can be combined with other aspects to reveal additional issues/problems that otherwise would not have been indicated or discovered based on condition monitoring alone. An example of this is continuous quality control that firstly checks that the input is within accepted ranges, secondly that the process parameters are fine, and thirdly that the output produced meets the expected requirements, etc. Thus, if output problems are detected and all the others look acceptable, it is an indication that the equipment needs maintenance or the process should be adjusted.
To achieve advanced condition monitoring, it is important that it has already been considered during the design stage so that the necessary sensors are included, data can be extracted at the rates needed, and that it is possible to add additional sensors later if required. Otherwise, it will be hard to successfully and economically perform condition monitoring that results in the required business value. In addition, using the results of the condition monitoring in re-designs or designs of new models/versions is encouraged as many future problems can then be avoided (as well as achieving greater reliability and potentially also improved maintainability if components or sub-systems that are error-prone are made easy to service and change parts).
4.3.2.1.
Scope and ambitionHow to use and get applications for domain users defines the scope. Intelligent services will provide knowledge and information to the user in a normal and transparent way. Digital industry results should be used by a normal engineer. He/she does not have to be a skilled specialist in programming or data science.
Since industry has been digital in many ways for decades and in growing proportions, it has also developed its own system engineering concepts, tools, languages, platforms and standards. Examples include PLCs, DCSs, alarm systems, CAD, etc. Today, this technology basis is drastically expanding to the variety of concepts and technologies, grouped conveniently under the title cyber-physical systems or industrial Internet. Machine learning, big data, deep learning and artificial intelligence are significant examples. What is still striking is that bringing these technologies into industry tends to depend on research initiatives, pilot experiments, proofs of concept, or in making real applications tailored, brittle, non-transparent and difficult to understand and manage. In other words, they are expensive or untrustworthy, or too low-level to be practical. Yesterday’s technologies are engineered in place, which is very beneficial and practical; there is no need for experimenting or science. Reference architectures, design languages, application generators, design automation and respective standardisation are obviously constituents of such engineerable new solutions.
For condition monitoring, we can list specifically:
Continuous/online/real-time monitoring of industrial equipment.
High-resolution/continuous/online/real-time monitoring of environmental parameters for industrial farming.
Fleet management, i.e. managing fleets of machinery, local and remote, benefiting from larger sets of similar components, etc., distributing experience, understanding common or similar characteristics and context-specific characteristics.
Modelling and integration of processes and equipment.
Benefiting from, or taking into account of, online conditions in other applications of a digital twin – i.e. MES, ERP, automation.
Hybrid/linked simulation and analysis.
Flexibility and robustness of production process, enabled by monitoring and predictions.
Adopting of 5G to condition monitoring – this may become a game changer.
4.3.2.2.
Competitive situation and game changersThe main players are coming from the US. They are dominating cloud-based solutions. However, the emergence of local edge-based intelligence offers an opportunity for Europe.
The biggest AI and machine learning acquisitions will continue with the acquisition by Facebook of Ozlo, Google of Kaggle and Halli Labs, as well as of AIMatter, Microsoft of Maluuba, Apple of Realface and Lattice, Amazon of Harvest.ai and Spotify of Niland.
The interest is very high and many are realising the potential benefits that can be obtained by using condition monitoring. On the ‘use’-side, it is expected that those who use condition monitoring will be more competitive and profitable than those not using it. Furthermore, on the provider side, large companies are showing increasing interest in condition monitoring systems and investing in the market. Larger provider players include IBM, Schneider Electric, Microsoft, SKF and Bosch.
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4.3.2.3.
Expected achievements Capabilities to build digital industry with outperforming business.
IoT devices supporting wireless and Ethernet-based connectivity.
Tools for engineers to use and get information and knowledge at all levels of personnel.
The expected achievements are improved overall equipment efficiency and profitability through increased efficiency, flexibility and robustness of the production process. Applying these hardware and software concepts in the farming sector will lead to further intensification of agriculture production and reduce environmental impacts. This is enabled by improved risk management using condition monitoring and predictive ability.