Fault Diagnosis Method of Rolling Bearing Based on MSCNN-CAM
In the fault diagnosis of rolling bearings, traditional deep learning models such as convolutional neural network (CNN) may be difficult to extract multi-scale information from complex vibration signals effectively by using single-scale convolution kernel. Aiming at this problem, a fault diagnosis m...
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Published in | 2024 Prognostics and System Health Management Conference (PHM) pp. 328 - 333 |
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Main Authors | , , , , , , |
Format | Conference Proceeding |
Language | English |
Published |
IEEE
28.05.2024
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Subjects | |
Online Access | Get full text |
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Summary: | In the fault diagnosis of rolling bearings, traditional deep learning models such as convolutional neural network (CNN) may be difficult to extract multi-scale information from complex vibration signals effectively by using single-scale convolution kernel. Aiming at this problem, a fault diagnosis method based on multi-scale attention mechanism is proposed. Multi-scale convolutional neural network (MSCNN) is introduced in this method, and information is extracted from different scales by using convolutional kernels of different sizes to prevent the loss of important features in the extraction process. In addition, a channel attention mechanism module (CAM) is introduced to selectively filter out irrelevant features after multi-scale information extraction. The effectiveness of this method is tested on the bearing data sets of CWRU and QPZZ-II. The experiment of adding noise to data sets shows that the model has high fault classification accuracy, robustness and strong anti-noise ability. |
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ISSN: | 2166-5656 |
DOI: | 10.1109/PHM61473.2024.00065 |