Unsupervised Anomaly Detection Using Optimal Transport for Predictive Maintenance

Anomaly detection is of crucial importance in industrial environment, especially in the context of predictive maintenance. As it is very costly to add an extra monitoring layer on production machines, non-invasive solutions are favored to watch for precursory clue indicating the possible need for a maintenance operation. Those clues are to be detected in evolving and highly variable working environment, calling for online and unsupervised methods. This contribution proposes a framework grounded in optimal transport, for the specific characterization of a system and the automatic detection of abnormal events. This method is evaluated on acoustic dataset and demonstrate the superiority of metrics derived from optimal transport on the Euclidean ones. The proposed method is shown to outperform one-class SVM on real datasets, which is the state-of-the-art method for anomaly detection.