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| Bearing Fault Diagnosis Method Based on VMD and Convolutional Neural Network Undervarying Operation Conditions |
| CHEN Jian1,2,HUANG Kai-xuan1,LÜ Wu-yang1,LIU Yuan-yuan1,YANG Bin 1,LIU Xing-fu1,CAI Kun-qi1 |
1. Institute of Sound and Vibration Research, Hefei University of Technology, Hefei,Anhui 230009, China
2. Automotive NVH Engineering & Technology Research Center Anhui Province, Hefei,Anhui 230009, China |
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Abstract To investigate the problem that it was difficult to obtain a large number of bearing fault data and diagnosefault type under varying operation conditions, a bearing fault diagnosis method based on variational mode decomposition and convolution neural network was proposed. This method could diagnose bearing data under varying operation conditions by using training data under steady conditions.Firstly, variational mode decomposition was used to decompose the bearing vibration signals in order to obtain a series of band-limited intrinsic modal functions.Then, convolution neural network was constructed to adaptiveextract and classifiy featuresof the IMFs, with optimization technology used to improve its adaptability.Finally, the rolling bearing fault data obtained from bench test was used in experimental verification, and the model of ResNet and SVM were used as comparison. The results showed that the diagnosis/recognition rate of the model is 100% / 98.86%under varying operation conditions that is higher than two comparison models, which also proved that the model can effectively realize bearing fault diagnosisunder varying operation conditions.
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Received: 05 February 2020
Published: 15 July 2021
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| Fund:National Natural Science Foundation Youth Fund Project |
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2021, Vol. 42 
