Process mining is a set of techniques in the field of process management that have primarily been used to analyse business processes, for example for the optimisation of enterprise resources. In this research, the feasibility of using process mining techniques for the analysis of event data from machine logs is investigated. A novel methodology, based on process mining, for profiling abnormal machine behaviour is proposed. Firstly, a process model is constructed from the event logs of the healthy machines. This model can subsequently be used as a benchmark to compare process models of other machines by means of conformance checking. This comparison results in a set of conformance scores related to the structure of the model and other more complex aspects such as the differences in duration of particular traces, the time spent in individual events, and the relative path frequency. The identified differences can subsequently be used as a basis for root cause analysis. The proposed approach is evaluated on a real-world industrial data set from the renewable energy domain, more specifically event logs of a fleet of inverters from several solar plants.

流程挖掘是流程管理领域中的一组技术，主要用于分析业务流程，例如用于优化企业资源。在这项研究中，研究了使用过程挖掘技术分析来自机器日志的事件数据的可行性。提出了一种新的基于过程挖掘的方法，用于分析异常机器行为。首先，从健康机器的事件日志中构建过程模型。该模型随后可以用作基准，通过一致性检查来比较其他机器的过程模型。这种比较得出了一组与模型的结构以及其他更复杂方面相关的一致性得分，例如特定轨迹的持续时间差异，在单个事件中花费的时间，和相对路径频率 所识别的差异随后可以用作根本原因分析的基础。在可再生能源领域的实际工业数据集上评估了所提出的方法，更具体地说，是来自多个太阳能发电厂的一组逆变器的事件日志。