Ergebnisse

@inproceedings{wibetabrock2023cutting,

title = {Cutting Through the Noise: An Empirical Comparison of Psycho-Acoustic and Envelope-based Features for Machinery Fault Detection},

author = {Peter Wißbrock and Yvonne Richter and David Pelkmann and Zhao Ren and Gregory Palmer },

url = {https://arxiv.org/pdf/2211.01704.pdf

https://ieeexplore.ieee.org/document/10095756},

year  = {2023},

date = {2023-05-05},

urldate = {2023-05-05},

booktitle = {ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},

pages = {1-5},

organization = {IEEE},

abstract = {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).},

keywords = {Assembly Line Inspection, Envelope Spectrum, Gear Fault Detection, IIP-Ecosphere, Industrial Noise, Psychoacoustics},

pubstate = {published},

tppubtype = {inproceedings}

}