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arXiv · 2107.09519

Canonical Polyadic Decomposition and Deep Learning for Machine Fault Detection

Gaetan Frusque·Gabriel Michau·Olga Fink
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Abstract

Acoustic monitoring for machine fault detection is a recent and expanding research path that has already provided promising results for industries. However, it is impossible to collect enough data to learn all types of faults from a machine. Thus, new algorithms, trained using data from healthy conditions only, were developed to perform unsupervised anomaly detection. A key issue in the development of these algorithms is the noise in the signals, as it impacts the anomaly detection performance. In this work, we propose a powerful data-driven and quasi non-parametric denoising strategy for spectral data based on a tensor decomposition: the Non-negative Canonical Polyadic (CP) decomposition. This method is particularly adapted for machine emitting stationary sound. We demonstrate in a case study, the Malfunctioning Industrial Machine Investigation and Inspection (MIMII) baseline, how the use of our denoising strategy leads to a sensible improvement of the unsupervised anomaly detection. Such approaches are capable to make sound-based monitoring of industrial processes more reliable.

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Gaetan Frusque, Gabriel Michau, Olga Fink. 2021-07-20. Canonical Polyadic Decomposition and Deep Learning for Machine Fault Detection. https://arxiv.org/abs/2107.09519

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