Details
| Original language | English |
|---|---|
| Title of host publication | IEEE International Conference on Acoustics, Speech and Signal Processing |
| Subtitle of host publication | ICASSP 2023 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Number of pages | 5 |
| ISBN (electronic) | 9781728163277 |
| ISBN (print) | 978-1-7281-6328-4 |
| Publication status | Published - 2023 |
| Event | 48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023 - Rhodes Island, Greece Duration: 4 Jun 2023 → 10 Jun 2023 |
Publication series
| Name | ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings |
|---|---|
| Volume | 2023-June |
| ISSN (Print) | 1520-6149 |
Abstract
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).
Keywords
- Assembly Line Inspection, Envelope Spectrum, Gear Fault Detection, Industrial Noise, Psychoacoustics
ASJC Scopus subject areas
- Computer Science(all)
- Software
- Computer Science(all)
- Signal Processing
- Engineering(all)
- Electrical and Electronic Engineering
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IEEE International Conference on Acoustics, Speech and Signal Processing: ICASSP 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2023-June).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
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 -