Industry 4.0 is a term that describes the fourth generation of industrial activity which is enabled by smart systems and Internet-based solutions. Two of the characteristic features of Industry 4.0 are computerization by utilizing cyber-physical systems and intelligent factories that are based on the concept of "internet of things". Maintenance is one of the application areas, referred to as maintenance 4.0, in form of self-learning and smart systems that predicts failure, makes diagnosis and triggers maintenance.
Indeed, assets are complex mixes of complex systems. Each system is built from components which, over time, may fail. When a component does fail, it is difficult to identify because the effects or problems that the failure has on the system are often neither obvious in terms of their source, nor unique. Previously the diagnosis of problems occurring in systems has been performed by experienced personnel with in-depth training and experience. Computer-based systems are now being used to automatically diagnose problems to overcome some of the disadvantages associated with relying on experienced personnel.
There is a clear need to be able to quickly and efficiently determine the cause of failures and propose optimum maintenance decisions, while minimizing the need for human intervention, but the above approaches either take a considerable amount of time before failures are diagnosed, or provide less than reliable results, or are unable to work well in complex systems. They also rely on a limited range of technical data regarding the system, component and the failure modes. The focus must be widened to properly reflect the complexity of the decision making situation, which relies on extensive knowledge about the asset.
Thus, for complex assets, much information needs to be captured and mined to assess the overall condition of the whole system. Therefore the integration of asset information is required to get an accurate health assessment of the whole system, i.e. infrastructure, factories; facilities, vehicles etc.., and determine the probability of a shutdown or slowdown. Moreover, the data collected are not only huge but often dispersed across independent systems that are difficult to access, fuse and mine due to disparate nature and granularity. In this scenario, ICT play a key role from sensor level to decision making process supporting operators and maintainers in the daily work. Technologies like sensor networks, sensor fusion, cloud computing, data mining, big data analytics and others increase their relevance in this domain due to the need of data handling and become the real trigger for the fourth industrial revolution.
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