Assessment of Explainable Anomaly Detection for Monitoring of Cold Rolling Process

The detection and explanation of anomalies within the industrial context remains a difficult task, which requires the use of well-designed methods. In this study, we focus on evaluating the performance of Explainable Anomaly Detection (XAD) algorithms in the context of a complex industrial process, specifically cold rolling. We train several state-of-the-art anomaly detection algorithms on the synthetic data from the cold rolling process and optimize their hyperparameters to maximize its predictive capabilities. Then we employ various model-agnostic Explainable AI (XAI) methods to generate explanations for the abnormal observations. The explanations are evaluated using a set of XAI metrics specifically selected for the anomaly detection task in industrial setting. The results provide insights into the impact of the selection of both machine learning and XAI methods on the overall performance of the model, emphasizing the importance of interpretability in industrial applications. For the detection of anomalies in cold rolling, we found that autoencoder-based approaches outperformed other methods, with the SHAP method providing the best explanations according to the evaluation metrics used.