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Keywords = Mixed Attention Residual Network (MARN)

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19 pages, 28563 KB  
Article
Intelligent Fault Diagnosis of Rolling Bearings Based on Markov Transition Field and Mixed Attention Residual Network
by Anshi Tong, Jun Zhang, Danfeng Wang and Liyang Xie
Appl. Sci. 2024, 14(12), 5110; https://doi.org/10.3390/app14125110 - 12 Jun 2024
Cited by 9 | Viewed by 1917
Abstract
To address the problems of existing methods that struggle to effectively extract fault features and unstable model training using unbalanced data, this paper proposes a new fault diagnosis method for rolling bearings based on a Markov Transition Field (MTF) and Mixed Attention Residual [...] Read more.
To address the problems of existing methods that struggle to effectively extract fault features and unstable model training using unbalanced data, this paper proposes a new fault diagnosis method for rolling bearings based on a Markov Transition Field (MTF) and Mixed Attention Residual Network (MARN). The acquired vibration signals are transformed into two-dimensional MTF feature images as network inputs to avoid the loss of the original signal information, while retaining the temporal correlation; then, the mixed attention mechanism is inserted into the residual structure to enhance the feature extraction capability, and finally, the network is trained and outputs diagnostic results. In order to validate the feasibility of the MARN, other popular deep learning (DL) methods are compared on balanced and unbalanced datasets divided by a CWRU fault bearing dataset, and the proposed method results in superior performance. Ultimately, the proposed method achieves an average recognition accuracy of 99.5% and 99.2% under the two categories of divided datasets, respectively. Full article
(This article belongs to the Collection Bearing Fault Detection and Diagnosis)
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