In current efforts to digitize manufacturing and move it into the fourth stage of the industrial revolution, a wide range of integration solutions is being considered to enable manufacturing to adapt to change. In transforming a factory into a self-organized, autonomous factory, companies are currently struggling with rapidly changing requirements and production factors, among other things. This is a particular problem for the human being as an actor within the factory, as the amount of new technologies and protocols increases the training effort. Proprietary interfaces of the control providers, a wide range of different communication protocols, complicate the understanding of the production processes, the evaluation and testability of new use cases and increase the danger of creating silos of knowledge as well as building collaboration barriers. As a solution to these problems, we propose an open software platform and define a way to model use case driven domain specific asset representation (DSA) that focuses on the human being and his needs for representing the factory in a way that it meets his requirements for the current production needs. We therefore conducted research on google scholar on human factors in industry 4.0 and used technologies as well as already existing platforms and their architecture.
Background: Today’s production facilities must be efficient in both manufacturing and maintenance. Efficiency enables the company to maintain the required output while reducing production effort or costs. With the increasing interest in process automation and the Internet of things since Industry 4.0 was introduced, such shop floors are growing in complexity. Every component of the production needs to be continuously monitored, which is the basis for predictive maintenance (PdM). To predict when maintenance is needed, the components’ conditions are monitored with the help of a condition monitoring (CM) system. However, this task is difficult for human employees, as the monitoring and analysis is very demanding. To overcome this, machine learning (ML) can be applied to ensure more efficient production. Methods: This paper aims to investigate the application of ML techniques for CM and PdM in the manufacturing sector. For this reason, a systematic mapping study (SMS) is conducted in order to structure and classify the current state of research and identify potential gaps for future investigation. Relevant literature was considered between January 2011 and May 2021. Results: Based on the guidelines for SMSs and previously defined research questions, existing publications are examined and a systematic overview of the current state of the research domain is provided. Conclusions: Techniques such as reinforcement learning and transfer learning are underrepresented, but increasingly attracting more attention. The findings of this study suggest that the most promising results belong to the applications of hybrid ML methods, where a set of methods are combined to build a more powerful model.
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