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APPLICATION OF COMPRESSIVE SENSING AND IMPROVED DEEP WAVELET NEURAL NETWORK IN BEARING FAULT DIAGNOSIS
Aiming at the problems of traditional bearings fault diagnosis methods had such shortcomings as largely dependent on expert prior knowledge and difficulty in fault feature extraction,a method based on compressive sensing（CS） and improved deep wavelet neural network（DWNN） was proposed. Firstly,the collected vibration data of bearings were de-noised and compressed by CS. Secondly,the improved wavelet auto-encoder was designed to construct the DWNN,and the " crosslayer" connection was introduced to alleviate the gradient disappearance of the network. Finally,unsupervised pre-training of DWNN was performed using a large amount of unlabeled compressed data and supervised and fine-tuned with a small amount of tagged data to realize fault discrimination. The experimental results show that the method can effectively identify the bearings with multiple fault types and multiple fault severities,which is less affected by prior knowledge and subjective knowledge and avoids complex artificial feature extraction process. The feature extraction ability and recognition ability of proposed method are superior than artificial neural network,deep belief network,deep sparse auto-encoder and so on.
APPLICATION OF COMPRESSIVE SENSING AND IMPROVED DEEP WAVELET NEURAL NETWORK IN BEARING FAULT DIAGNOSIS
Aiming at the problems of traditional bearings fault diagnosis methods had such shortcomings as largely dependent on expert prior knowledge and difficulty in fault feature extraction,a method based on compressive sensing（CS） and improved deep wavelet neural network（DWNN） was proposed. Firstly,the collected vibration data of bearings were de-noised and compressed by CS. Secondly,the improved wavelet auto-encoder was designed to construct the DWNN,and the " crosslayer" connection was introduced to alleviate the gradient disappearance of the network. Finally,unsupervised pre-training of DWNN was performed using a large amount of unlabeled compressed data and supervised and fine-tuned with a small amount of tagged data to realize fault discrimination. The experimental results show that the method can effectively identify the bearings with multiple fault types and multiple fault severities,which is less affected by prior knowledge and subjective knowledge and avoids complex artificial feature extraction process. The feature extraction ability and recognition ability of proposed method are superior than artificial neural network,deep belief network,deep sparse auto-encoder and so on.
APPLICATION OF COMPRESSIVE SENSING AND IMPROVED DEEP WAVELET NEURAL NETWORK IN BEARING FAULT DIAGNOSIS
DU XiaoLei (author) / CHEN ZhiGang (author) / ZHANG Nan (author) / XU Xu (author)
2020
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
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