To build, run, and maintain reliable manufacturing machines, the condition of their components has to be continuously monitored. When following a fine-grained monitoring of these machines, challenges emerge pertaining to the (1) feeding procedure of large amounts of sensor data to downstream processing components and the (2) meaningful analysis of the produced data. Regarding the latter aspect, manifold purposes are addressed by practitioners and researchers. Two analyses of real-world datasets that were generated in production settings are discussed in this paper. More specifically, the analyses had the goals (1) to detect sensor data anomalies for further analyses of a pharma packaging scenario and (2) to predict unfavorable temperature values of a 3D printing machine environment. Based on the results of the analyses, it will be shown that a proper management of machines and their components in industrial manufacturing environments can be efficiently supported by the detection of anomalies. The latter shall help to support the technical evangelists of the production companies more properly.

The increasing adoption of process-aware information systems (PAISs) has resulted in large process model collections. To support users having different perspectives on these processes and related data, a PAIS should provide personalized views on process models. Existing PAISs, however, do not provide mechanisms for creating or even changing such process views. Especially, changing process models is a frequent use case in PAISs due to changing needs or unplanned situations. While process views have been used as abstractions for visualizing large process models, no work exists on how to change process models based on respective views. This paper presents an approach for changing large process models through updates of corresponding process views, while ensuring up-to-dateness and consistency of all other process views on the process model changed. Respective update operations can be applied to a process view and corresponding changes be correctly propagated to the underlying process model. Furthermore, all other views related to this process model are then migrated to the new version of the process model as well. Overall, our view framework enables domain experts to evolve large process models over time based on appropriate model abstractions.

The increasing interconnection of machines in industrial production on one hand, and the improved capabilities to store, retrieve, and analyze large amounts of data on the other, offer promising perspectives for maintaining production machines. Recently, predictive maintenance has gained increasing attention in the context of equipment maintenance systems. As opposed to other approaches, predictive maintenance relies on machine behavior models, which offer several advantages. In this highly interdisciplinary field, there is a lack of a literature review of relevant research fields and realization techniques. To obtain a comprehensive overview on the state of the art, large data sets of relevant literature need to be considered and, best case, be automatically partitioned into relevant research fields. A proper methodology to obtain such an overview is the bibliometric analysis method. In the presented work, we apply a bibliometric analysis to the field of equipment maintenance systems. To be more precise, we analyzed clusters of identified literature with the goal to obtain deeper insight into the related research fields. Moreover, cluster metrics reveal the importance of a single paper and an investigation of the temporal cluster development indicates the evolution of research topics. In this context, we introduce a new measure to compare results from different time periods in an appropriate way. In turn, among others, this simplifies the analysis of topics, with a vast amount of subtopics. Altogether, the obtained results particularly provide a comprehensive overview of established techniques and emerging trends for equipment maintenance systems.

Assembly, configuration, maintenance, and repair processes in cyber-physical systems (e.g., a press line in a plant) comprise a multitude of complex tasks, whose execution needs to be controlled, coordinated and monitored. Amongst others, a process-centric guidance of users (e.g. service operators) is required, taking the high variability in the assembly of cyberphysical systems (e.g. press line variability) into account. Moreover, the tasks to be performed along these processes may be related to physical components, sensors and actuators, which need to be properly recognized, integrated and operated. In order to digitize cyber-physical processes as well as to guide users in a process-centric way, therefore, we suggest integrating process management technology, sensor/actuator interfaces, and augmented reality techniques. The paper discusses fundamental requirements for such an integration and presents an approach for process-centric user guidance that combines context and process management with augmented reality enhanced tasks. For evaluation purposes, we analyzed the cyber-physical processes of pharmaceutical packaging machines and implemented selected ones based on the approach. Overall, we are able to demonstrate the usefulness of context-aware process management for the flexible support of cyber-physical processes in the Industrial Internet of Things.

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