A Deep Learning Method for Bearing Cross-Domain Fault Diagnostics Based on the Standard Envelope Spectrum

Intelligent fault diagnostics based on deep learning provides a favorable guarantee for the reliable operation of equipment, but a trained deep learning model generally has low prediction accuracy in cross-domain diagnostics. To solve this problem, a deep learning fault diagnosis method based on the...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2024-05, Vol.24 (11), p.3500
Main Authors: Zhai, Lubin, Wang, Xiufeng, Si, Zeyiwen, Wang, Zedong
Format: Article
Language:English
Subjects:
Bearings
Classification
Contact angle
convolutional neural networks
cross-domain fault diagnostics
Deep learning
Failure
Fourier transforms
Machine learning
Methods
Neural networks
rolling bearings
Signal processing
Spectrum analysis
standardized envelope spectrum
Vibration
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Summary:Intelligent fault diagnostics based on deep learning provides a favorable guarantee for the reliable operation of equipment, but a trained deep learning model generally has low prediction accuracy in cross-domain diagnostics. To solve this problem, a deep learning fault diagnosis method based on the reconstructed envelope spectrum is proposed to improve the ability of rolling bearing cross-domain fault diagnostics in this paper. First, based on the envelope spectrum morphology of rolling bearing failures, a standard envelope spectrum is constructed that reveals the unique characteristics of different bearing health states and eliminates the differences between domains due to different bearing speeds and bearing models. Then, a fault diagnosis model was constructed using a convolutional neural network to learn features and complete fault classification. Finally, using two publicly available bearing data sets and one bearing data set obtained by self-experimentation, the proposed method is applied to the data of the fault diagnostics of rolling bearings under different rotational speeds and different bearing types. The experimental results show that, compared with some popular feature extraction methods, the proposed method can achieve high diagnostic accuracy with data at different rotational speeds and different bearing types, and it is an effective method for solving the problem with cross-domain fault diagnostics for rolling bearings.
ISSN:1424-8220
1424-8220
DOI:10.3390/s24113500