Canonical Polyadic Decomposition and Deep Learning for Machine Fault Detection
This work addresses noise reduction for acoustic monitoring in industrial processes, making fault detection more reliable, but it is incremental as it builds on existing tensor decomposition methods.
The paper tackles the problem of noise in acoustic signals for unsupervised machine fault detection by proposing a denoising strategy based on Non-negative Canonical Polyadic decomposition, which leads to a sensible improvement in anomaly detection performance as demonstrated on the MIMII baseline.
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.