Explaining Anomalies in Industrial Multivariate Time-series Data with the help of eXplainable AI

The large amount of data generated by industrial plants provides an excellent opportunity to use Machine Learning (ML) for a better understanding of plant behaviour. Thus, supporting plants operators in running their plants efficiently. An example of an anomaly detection method is to help plant operators detect if their plant is still running normally or if curative actions are needed. Users of ML-based solutions often complain of a challenge called lack of interpretability, i.e., the degree to which one can understand the model's outcome. To address the need for better interpretability of ML models, the research field eXplainable Artificial Intelligence (XAI) has recently received increasing attention in the industrial domain. This paper performs a survey and investigates different XAI techniques that can be applied for detecting anomalies in industrial plant assets. The paper focuses on multivariate time-series data because a) it is the most predominant type of data in industrial systems and b) all the available XAI techniques have applications for various data types such as images, tabular, or textual data. However, these techniques are generally not well suited to explain anomalies in multivariate time-series. To solve this problem, we build an anomaly detection method for the multivariate time-series data generated by the industrial simulators using auto-encoders. Based on feature attribution, examples, and trees, seven different XAI techniques are ideated, developed, and discussed. Out of the seven XAI techniques, a SHAP based explainer, called DTFS, correctly identified the root cause of the anomaly with an accuracy of 86% and took 1.53 seconds to explain our benchmark system.