Unsupervised anomaly detection using optimal transport for predictive maintenance

Abstract : 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 met-rics 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.