Academic Journal
Bearings fault diagnosis method based on MAM and deep separable dilated convolutional neural network
| العنوان: | Bearings fault diagnosis method based on MAM and deep separable dilated convolutional neural network |
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| المؤلفون: | Lei, Chunli, Shi, Jiashuo, Ma, Shuzhen, Xue, Linlin, Jiao, Mengxuan, Li, Jianhua |
| المساهمون: | Graduate Innovation Star Project of the Education Department of Gansu Province, National Natural Science Foundation of China, Natural Science Foundation of Gansu Province |
| المصدر: | Measurement Science and Technology ; volume 34, issue 11, page 114001 ; ISSN 0957-0233 1361-6501 |
| بيانات النشر: | IOP Publishing |
| سنة النشر: | 2023 |
| الوصف: | Aiming at the problems of traditional fault diagnosis methods that do not represent the time correlation between signals, low recognition accuracy under complex working conditions and noise interference and too many parameters, a bearing fault diagnosis method based on mixed attention mechanism (MAM) and deep separable dilated convolution neural network (DSDCNN) is proposed. Firstly, a Markov transfer field encoding method is used to transform the original one-dimensional vibration signal into a two-dimensional feature image with temporal correlation. Secondly, a deep separable convolution algorithm is presented by taking advantage of the low computational complexity of deep separable convolution and the ability of dilated convolution to expand the receptive field under the condition of invariable number of parameters. Then, the MAM is designed to make the model capture the feature dependency of the feature map in spatial and channel dimensions, and the MAM-DSDCNN model is constructed. Finally, the fault diagnosis performance of the proposed model is verified with two different data sets. The results show that the average recognition accuracy of MAM-DSDCNN reaches 99.63% under variable load conditions, 99.42% under variable speed conditions, 94.26% under noisy environment with the signal-to-noise of 0 dB, which prove that the model has higher recognition accuracy, stronger generalization and noise immunity performance than other deep learning algorithms. |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | unknown |
| DOI: | 10.1088/1361-6501/ace642 |
| DOI: | 10.1088/1361-6501/ace642/pdf |
| الاتاحة: | http://dx.doi.org/10.1088/1361-6501/ace642 https://iopscience.iop.org/article/10.1088/1361-6501/ace642 https://iopscience.iop.org/article/10.1088/1361-6501/ace642/pdf |
| Rights: | https://iopscience.iop.org/page/copyright ; https://iopscience.iop.org/info/page/text-and-data-mining |
| رقم الانضمام: | edsbas.2BC8CBE9 |
| قاعدة البيانات: | BASE |
| DOI: | 10.1088/1361-6501/ace642 |
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