A Process Mining-based unsupervised Anomaly Detection technique for the Industrial Internet of Things

Industrial Internet of Things (IIoT) applications in Industry 4.0 collect and process Time Series (TS) originating from heterogeneous sources. Many data-driven techniques have been proposed over the years for unsupervised collective Anomaly Detection (AD) to detect anomalous TS and improve quality of service. These techniques build statistical and/or behavioral models through data-driven algorithms often based on machine and deep learning. However, these algorithms may have black-box behavior, may require too much computational effort, and may not be trustworthy. In order to address these challenges, this paper proposes and evaluates an unsupervised AD technique that pre-processes noisy and complex TS and detects anomalous patterns. Pre-processing handles TS complexity through Feature Extraction and Dimensionality Reduction based on Autoencoders (AEs), whereas anomalous patterns are classified through Process Mining (PM), which unsupervisedly captures TS patterns and compares unknown behavior to such patterns for collective AD. We apply the technique to a scale replica of an assembly plant adopted in a smart automotive factory to evaluate our technique with respect to several configurations to analyze their impact on both detection and timing performance. Our contribution improves and extends the existing state-of-the-art work in the literature regarding the application of PM to IIoT for collective AD in TS. Moreover, it compares the obtained results with a baseline approach based on AEs.