TY - GEN

T1 - Cutting Through the Noise

T2 - 48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

AU - Wißbrock, Peter

AU - Richter, Yvonne

AU - Pelkmann, David

AU - Ren, Zhao

AU - Palmer, Gregory

N1 - Funding Information: Parts of this article were supported by the Ministry of Economic Affairs, Innovation, Digitization, and Energy of North Rhine Westphalia through the excellence cluster itsOWL in the projects “PsyMe” and “ML4Pro2” and the German Federal Ministry for Economics and Climate Action (BMWK) through the research project “IIP-Ecosphere”, via funding code 01MK20006A. 1Part of the data will be provided by request (for research only) signals. However, acoustic-based fault detection has received limited attention, due to the risk of background noise in an industrial environment causing misclassification [3]. Nevertheless, acoustic signals have many advantages compared to vibration signals, while capable of delivering a comparable performance [1, 7]. For instance, no connection is needed between the motor and the sensor, and acoustic signals cover more types of faults including aerodynamic components [8]. As discussed in previous work [5], in real-world machinery fault detection we are facing small imbalanced datasets with less samples from faulty motors. To design a realistic pipeline, we focus on one-class classifier (OCC), trained on samples of healthy motors only.

PY - 2023

Y1 - 2023

N2 - Acoustic-based fault detection has been one of the key instruments 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).

AB - Acoustic-based fault detection has been one of the key instruments 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).

KW - Assembly Line Inspection

KW - Envelope Spectrum

KW - Gear Fault Detection

KW - Industrial Noise

KW - Psychoacoustics

UR - http://www.scopus.com/inward/record.url?scp=85177598781&partnerID=8YFLogxK

U2 - 10.48550/arXiv.2211.01704

DO - 10.48550/arXiv.2211.01704

M3 - Conference contribution

AN - SCOPUS:85177598781

SN - 978-1-7281-6328-4

T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

BT - IEEE International Conference on Acoustics, Speech and Signal Processing

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 4 June 2023 through 10 June 2023

ER -