Wu, Y., Yang, Y., Yang, Z. et al. (5 more authors) (2025) Multi‐channel deep pulse‐coupled net: a novel bearing fault diagnosis framework. IET Image Processing, 19 (1). e70033. ISSN 1751-9659
Abstract
Bearings are a critical part of various industrial equipment. Existing bearing fault detection methods face challenges such as complicated data preprocessing, difficulty in analysing time series data, and inability to learn multi-dimensional features, resulting in insufficient accuracy. To address these issues, this study proposes a novel bearing fault diagnosis model called multi-channel deep pulse-coupled net (MC-DPCN) inspired by the mechanisms of image processing in the primary visual cortex of the brain. Initially, the data are transformed into greyscale spectrograms, allowing the model to handle time series data effectively. The method introduces a convolutional coupling mechanism between multiple channels, enabling the framework can learn the features on all channels well. This study conducted experiments using the bearing fault dataset from Case Western Reserve University. On this dataset, a 6-channel (adjustable to specific tasks) MC-DPCN was utilized to analyse one normal class and three fault classes. Compared to state-of-the-art bearing fault diagnosis methods, our model demonstrates one of the highest diagnostic accuracies. This method achieved an accuracy of 99.96% in normal vs. fault discrimination and 99.89% in fault type diagnosis (average result of ten-fold cross-validation).
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2025 The Author(s). IET Image Processing published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made. http://creativecommons.org/licenses/by-nc-nd/4.0/ |
Keywords: | Bearing Fault Diagnosis; brain-inspired computing; DPCNN; PCNN; Spectrogram; time series |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Electronic and Electrical Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 25 Mar 2025 10:57 |
Last Modified: | 25 Mar 2025 10:57 |
Status: | Published |
Publisher: | Institution of Engineering and Technology (IET) |
Refereed: | Yes |
Identification Number: | 10.1049/ipr2.70033 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:224793 |