Fault diagnosis of rigid guide based on wavelet transform and improved convolutional neural network
Some of the existing fault diagnosis methods for rigid guide are only suitable for small sample data sets. Although some methods are suitable for large sample data sets, they ignore the multi-condition background in the actual working environment. The method of rigid guide fault diagnosis based on t...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | zho |
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Editorial Department of Industry and Mine Automation
2022-09-01
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| Series: | Gong-kuang zidonghua |
| Subjects: | |
| Online Access: | http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671-251x.17964 |
| _version_ | 1797868969586065408 |
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| author | DU Fei MA Tianbing HU Weikang LYU Yinghui PENG Meng |
| author_facet | DU Fei MA Tianbing HU Weikang LYU Yinghui PENG Meng |
| author_sort | DU Fei |
| collection | DOAJ |
| description | Some of the existing fault diagnosis methods for rigid guide are only suitable for small sample data sets. Although some methods are suitable for large sample data sets, they ignore the multi-condition background in the actual working environment. The method of rigid guide fault diagnosis based on the convolutional neural network has the problems of huge data and computation, and easy to produce over-fitting. In order to solve these problems, a fault diagnosis method of rigid guide based on wavelet transform and improved convolutional neural network is proposed. Firstly, two kinds of defects, dislocation and gap, are set in the rigid cage guide. The vibration acceleration signals of the hoisting container under multiple working conditions are collected. Secondly, the collected vibration acceleration signals are converted into two-dimensional time-frequency images by wavelet transform. The time and frequency resolution of the two-dimensional time-frequency images processed by the Complex Morlet wavelet basis function is determined to be the best by trial and error method. Thirdly, the structure of the convolutional neural network model is adjusted. The first pooling layer and the fifth pooling layer are reserved. The second pool layer, the third pooling layer and the fourth pooling layer are replaced by small-scale convolutional layers to prevent the over-fitting phenomenon. Finally, the two-dimensional time-frequency image is input into the improved convolutional neural network model. The experimental results show the following points. ① After training, the average accuracy of the improved model is about 99% on the training set and 99.5% on the test set. ② When the training data reaches 200 steps, the accuracy of the improved model is more than 99%, and the loss function of the improved model approaches 0. These results show that the improved model has good convergence performance, and the generalization of the model is enhanced. The inhibition effect on over-fitting in the learning process is obvious. ③ On the confusion matrix of the validation set, the identification rate of gap defect and dislocation defects is 100%. The identification rate of no defect is 92%, and 8% of the defect are mistakenly identified as gap defects. ④ Compared with EMD-SVD-SVM, wavelet packet-SVM, EMD-SVD-BP neural network and wavelet packet-BP neural network, the accuracy of rigid guide fault diagnosis method based on wavelet transform and the improved convolutional neural network reaches 99%. |
| first_indexed | 2024-04-10T00:05:18Z |
| format | Article |
| id | doaj.art-2639285b21bf4f9187d0830324bba8a5 |
| institution | Directory Open Access Journal |
| issn | 1671-251X |
| language | zho |
| last_indexed | 2024-04-10T00:05:18Z |
| publishDate | 2022-09-01 |
| publisher | Editorial Department of Industry and Mine Automation |
| record_format | Article |
| series | Gong-kuang zidonghua |
| spelling | doaj.art-2639285b21bf4f9187d0830324bba8a52023-03-17T01:01:21ZzhoEditorial Department of Industry and Mine AutomationGong-kuang zidonghua1671-251X2022-09-014894248, 6210.13272/j.issn.1671-251x.17964Fault diagnosis of rigid guide based on wavelet transform and improved convolutional neural networkDU FeiMA TianbingHU Weikang0LYU Yinghui1PENG Meng2School of Mechanical Engineering, Anhui University of Science and Technology, Huainan 232001, ChinaSchool of Mechanical Engineering, Anhui University of Science and Technology, Huainan 232001, ChinaSchool of Mechanical Engineering, Anhui University of Science and Technology, Huainan 232001, ChinaSome of the existing fault diagnosis methods for rigid guide are only suitable for small sample data sets. Although some methods are suitable for large sample data sets, they ignore the multi-condition background in the actual working environment. The method of rigid guide fault diagnosis based on the convolutional neural network has the problems of huge data and computation, and easy to produce over-fitting. In order to solve these problems, a fault diagnosis method of rigid guide based on wavelet transform and improved convolutional neural network is proposed. Firstly, two kinds of defects, dislocation and gap, are set in the rigid cage guide. The vibration acceleration signals of the hoisting container under multiple working conditions are collected. Secondly, the collected vibration acceleration signals are converted into two-dimensional time-frequency images by wavelet transform. The time and frequency resolution of the two-dimensional time-frequency images processed by the Complex Morlet wavelet basis function is determined to be the best by trial and error method. Thirdly, the structure of the convolutional neural network model is adjusted. The first pooling layer and the fifth pooling layer are reserved. The second pool layer, the third pooling layer and the fourth pooling layer are replaced by small-scale convolutional layers to prevent the over-fitting phenomenon. Finally, the two-dimensional time-frequency image is input into the improved convolutional neural network model. The experimental results show the following points. ① After training, the average accuracy of the improved model is about 99% on the training set and 99.5% on the test set. ② When the training data reaches 200 steps, the accuracy of the improved model is more than 99%, and the loss function of the improved model approaches 0. These results show that the improved model has good convergence performance, and the generalization of the model is enhanced. The inhibition effect on over-fitting in the learning process is obvious. ③ On the confusion matrix of the validation set, the identification rate of gap defect and dislocation defects is 100%. The identification rate of no defect is 92%, and 8% of the defect are mistakenly identified as gap defects. ④ Compared with EMD-SVD-SVM, wavelet packet-SVM, EMD-SVD-BP neural network and wavelet packet-BP neural network, the accuracy of rigid guide fault diagnosis method based on wavelet transform and the improved convolutional neural network reaches 99%.http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671-251x.17964vertical shaft hoistingrigid guidefault diagnosisdislocation defectgap defectwavelet transformtwo-dimensional time-frequency imageconvolutional neural network |
| spellingShingle | DU Fei MA Tianbing HU Weikang LYU Yinghui PENG Meng Fault diagnosis of rigid guide based on wavelet transform and improved convolutional neural network Gong-kuang zidonghua vertical shaft hoisting rigid guide fault diagnosis dislocation defect gap defect wavelet transform two-dimensional time-frequency image convolutional neural network |
| title | Fault diagnosis of rigid guide based on wavelet transform and improved convolutional neural network |
| title_full | Fault diagnosis of rigid guide based on wavelet transform and improved convolutional neural network |
| title_fullStr | Fault diagnosis of rigid guide based on wavelet transform and improved convolutional neural network |
| title_full_unstemmed | Fault diagnosis of rigid guide based on wavelet transform and improved convolutional neural network |
| title_short | Fault diagnosis of rigid guide based on wavelet transform and improved convolutional neural network |
| title_sort | fault diagnosis of rigid guide based on wavelet transform and improved convolutional neural network |
| topic | vertical shaft hoisting rigid guide fault diagnosis dislocation defect gap defect wavelet transform two-dimensional time-frequency image convolutional neural network |
| url | http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671-251x.17964 |
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