A Three-Step Machine Learning Pipeline for Detecting and Explaining Anomalies in the Time Series of Industrial Process Plants.

Anomaly detection offers an important type of machine learning analysis for industrial automation systems because it is one of the highest goals in the industrial domain to avoid production disturbances and to stay productive. If devices are not operating anymore within normal bounds, or if the produced product quality is no longer within normal bounds, then the plant operator wants to be informed as early as possible. Anomaly detection can provide this type of information for the operator. This paper presents a solution pipeline for the detection and explanation of anomalies in the multivariate time series of industrial plant processes, and a possible implementation of this pipeline that is fully based on the use of neural network architectures. The pipeline consists of three consecutive steps, that build on each other, to successively explore in depth the anomalies and their underlying root-causes. The three steps of the pipeline are: 1) the detection of the anomaly itself, 2) pointing to the location where the anomaly comes from, and 3) determining the type of anomaly at this location. The evaluation of the pipeline is performed to detect a set of 16 simulated plant equipment failures that have been obtained from a real-world industrial process that is typically found in oil production fields, called the separator process.