Canonical polyadic decomposition and deep learning for machine fault detection
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
How to Cite
Canonical polyadic decomposition, Sound signal, Autoencoder, Deep learning, Spectrogram, Fault detection
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