ArXiv Preprint
Acoustic-based fault detection has a high potential to monitor the health
condition of mechanical parts. However, the background noise of an industrial
environment may negatively influence the performance of fault detection.
Limited attention has been paid to improving the robustness of fault detection
against industrial environmental noise. Therefore, we present the Lenze
production background-noise (LPBN) real-world dataset and an automated and
noise-robust auditory inspection (ARAI) system for the end-of-line inspection
of geared motors. An acoustic array is used to acquire data from motors with a
minor fault, major fault, or which are healthy. A benchmark is provided to
compare the psychoacoustic features with different types of envelope features
based on expert knowledge of the gearbox. To the best of our knowledge, we are
the first to apply time-varying psychoacoustic features for fault detection. We
train a state-of-the-art one-class-classifier, on samples from healthy motors
and separate the faulty ones for fault detection using a threshold. The
best-performing approaches achieve an area under curve of 0.87 (logarithm
envelope), 0.86 (time-varying psychoacoustics), and 0.91 (combination of both).
Peter Wißbrock, Yvonne Richter, David Pelkmann, Zhao Ren, Gregory Palmer
2022-11-03
