Real-time monitoring and optimization of production and logistics processes significantly improve the efficiency of production systems. Advanced production management solutions require real-time information about the status of products, production, and resources. As real-time locating systems (also referred to as indoor positioning systems) can enrich the available information, these systems started to gain attention in industrial environments in recent years. This paper provides a review of the possible technologies and applications related to production control and logistics, quality management, safety, and efficiency monitoring. This work also provides a workflow to clarify the steps of a typical real-time locating system project, including the cleaning, pre-processing, and analysis of the data to provide a guideline and reference for research and development of indoor positioning-based manufacturing solutions.

Alarm management is an important task to ensure the safety of industrial process technologies. A well-designed alarm system can reduce the workload of operators parallel with the support of the production, which is in line with the approach of Industry 5.0. Using Process Mining tools to explore the operator-related event scenarios requires a goal-oriented log file format that contains the start and the end of the alarms along with the triggered operator actions. The key contribution of the work is that a method is presented that transforms the historical event data of control systems into goal-oriented log files used as inputs of process mining algorithms. The applicability of the proposed process mining-based method is presented concerning the analysis of a hydrofluoric acid alkylation plant. The detailed application examples illustrate how the extracted process models can be interpreted and utilized. The results confirm that applying the tools of process mining in alarm management requires a goal-oriented log-file design.

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With the increasing complexity of production technologies, alarm management becomes more and more important in industrial process control. The overall safety of the plant relies heavily on the situation-aware response time of the staff. This kind of awareness has to be supported by a state-of-the-art alarm management system, which requires broad and up-to-date process-relevant knowledge. The proposed method provides a solution when such information is not fully available. With the utilization of machine learning algorithms, a real-time event scenario prediction can be gained by comparing the frequent event patterns extracted from historical event-log data with the actual online data stream. This study discusses an integrated solution, which combines sequence compression and sequence alignment to predict the most probable alarm progression. The effectiveness and limitations of the proposed method are tested using the data of an industrial delayed-coker plant. The results confirm that the presented parameter-free method identifies the characteristic patternsoperational statesand their progression with high confidence in real time, suggesting it for a wider adoption for sequence analysis.