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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.
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https://hal.uvsq.fr/hal-02541920
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Submitted on : Tuesday, April 14, 2020 - 11:42:26 AM
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Amina Alaoui-Belghiti, Sylvain Chevallier, Eric Monacelli. Unsupervised anomaly detection using optimal transport for predictive maintenance. International Conference on Artificial Neural Networks, Sep 2019, Munich, Germany. ⟨10.1007/978-3-030-30490-4_54⟩. ⟨hal-02541920⟩

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