Process mining

Frequent pattern mining-based log file partition for process mining

Process mining is a technique for exploring models based on event sequences, growing in popularity in the process industry. Process mining algorithms assume that the processed log files contain events generated by only one unknown process, which can lead to extremely complex and inaccurate models when this assumption is not met. To address this issue, this article proposes a frequent pattern mining-based method for log file partitioning, allowing for the exploration of parallel processes. The key idea is that frequent pattern mining can identify grouped events and generate sub-logs of overlapping sub-processes. Thanks to the pre-processing of the log files, more compact and interpretable process models can be identified. We developed a set of goal-oriented metrics to evaluate the complexity of process mining problems and the resulting models. The applicability and effectiveness of the method are demonstrated in the analysis of process alarms of an industrial plant. The results confirm that the proposed method enables the discovery of targeted sub-process models by partitioning the log file using frequent pattern mining, and the effectiveness of the method increases with the number of parallel processes stored in the same log file. We recommend applying the method in every case where there is no clear start and end of the logged events so that the log file can describe different processes.


Post date: 03 April 2023 

Simultaneous Process Mining of Process Events and Operator Actions for Alarm Management

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. 


 Post date: 29 September 2022 

Process mining in production systems

Due to the increasing automation and integrity of today’s productions systems, thousands of alarms are generated every day in the more and more complex process control units. The core concept of our work is the investigation of the application possibilities of the Process Mining Framework (PROM) for the mining of industrial alarm management databases. The task-oriented formalisation of input datasets in the form of XES files is presented. The applicability and effectiveness of the different plug-ins are shown in terms of the analysis of the alarm and event-log database of an industrial delayed-coker plant. The results illustrate that the presented tools are suitable for the tracking of activities at different levels of the hierarchy, for the detection of the spillover effect of different malfunctions and the detection of the changes in the operating conditions.