Improved Variational Mode Decomposition and CNN for Intelligent Rotating Machinery Fault Diagnosis
This paper proposes an intelligent diagnosis method for rotating machinery faults based on improved variational mode decomposition (IVMD) and CNN to process the rotating machinery non-stationary signal. Firstly, to solve the problem of time-domain feature extraction for fault diagnosis, this paper p...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-06-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/24/7/908 |
| _version_ | 1797439627872698368 |
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| author | Qiyang Xiao Sen Li Lin Zhou Wentao Shi |
| author_facet | Qiyang Xiao Sen Li Lin Zhou Wentao Shi |
| author_sort | Qiyang Xiao |
| collection | DOAJ |
| description | This paper proposes an intelligent diagnosis method for rotating machinery faults based on improved variational mode decomposition (IVMD) and CNN to process the rotating machinery non-stationary signal. Firstly, to solve the problem of time-domain feature extraction for fault diagnosis, this paper proposes an improved variational mode decomposition method with automatic optimization of the number of modes. This method overcomes the problems of the traditional VMD method, in that each parameter is set by experience and is greatly influenced by subjective experience. Secondly, the decomposed signal components are analyzed by correlation, and then high correlated components with the original signal are selected to reconstruct the original signal. The continuous wavelet transform (CWT) is employed to extract the two-dimensional time–frequency domain feature map of the fault signal. Finally, the deep learning method is used to construct a convolutional neural network. After feature extraction, the two-dimensional time-frequency image is applied to the neural network to identify fault features. Experiments verify that the proposed method can adapt to rotating machinery faults in complex environments and has a high recognition rate. |
| first_indexed | 2024-03-09T11:55:50Z |
| format | Article |
| id | doaj.art-b080a63e4735401c9f836e14db5ba8be |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-09T11:55:50Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-b080a63e4735401c9f836e14db5ba8be2023-11-30T23:09:15ZengMDPI AGEntropy1099-43002022-06-0124790810.3390/e24070908Improved Variational Mode Decomposition and CNN for Intelligent Rotating Machinery Fault DiagnosisQiyang Xiao0Sen Li1Lin Zhou2Wentao Shi3School of Artificial Intelligence, Henan University, Zhengzhou 450046, ChinaSchool of Artificial Intelligence, Henan University, Zhengzhou 450046, ChinaSchool of Artificial Intelligence, Henan University, Zhengzhou 450046, ChinaSchool of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, ChinaThis paper proposes an intelligent diagnosis method for rotating machinery faults based on improved variational mode decomposition (IVMD) and CNN to process the rotating machinery non-stationary signal. Firstly, to solve the problem of time-domain feature extraction for fault diagnosis, this paper proposes an improved variational mode decomposition method with automatic optimization of the number of modes. This method overcomes the problems of the traditional VMD method, in that each parameter is set by experience and is greatly influenced by subjective experience. Secondly, the decomposed signal components are analyzed by correlation, and then high correlated components with the original signal are selected to reconstruct the original signal. The continuous wavelet transform (CWT) is employed to extract the two-dimensional time–frequency domain feature map of the fault signal. Finally, the deep learning method is used to construct a convolutional neural network. After feature extraction, the two-dimensional time-frequency image is applied to the neural network to identify fault features. Experiments verify that the proposed method can adapt to rotating machinery faults in complex environments and has a high recognition rate.https://www.mdpi.com/1099-4300/24/7/908intelligent fault diagnosisimproved variational mode decompositiondeep learningcontinuous wavelet transform (CWT) |
| spellingShingle | Qiyang Xiao Sen Li Lin Zhou Wentao Shi Improved Variational Mode Decomposition and CNN for Intelligent Rotating Machinery Fault Diagnosis Entropy intelligent fault diagnosis improved variational mode decomposition deep learning continuous wavelet transform (CWT) |
| title | Improved Variational Mode Decomposition and CNN for Intelligent Rotating Machinery Fault Diagnosis |
| title_full | Improved Variational Mode Decomposition and CNN for Intelligent Rotating Machinery Fault Diagnosis |
| title_fullStr | Improved Variational Mode Decomposition and CNN for Intelligent Rotating Machinery Fault Diagnosis |
| title_full_unstemmed | Improved Variational Mode Decomposition and CNN for Intelligent Rotating Machinery Fault Diagnosis |
| title_short | Improved Variational Mode Decomposition and CNN for Intelligent Rotating Machinery Fault Diagnosis |
| title_sort | improved variational mode decomposition and cnn for intelligent rotating machinery fault diagnosis |
| topic | intelligent fault diagnosis improved variational mode decomposition deep learning continuous wavelet transform (CWT) |
| url | https://www.mdpi.com/1099-4300/24/7/908 |
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