Affiliation:
1. School of Mechanical Engineering, Henan Institute of Technology, Xinxiang 453003, China
2. Mechanical and Electrical Equipment Digital Design and Manufacturing Engineering Technology Research Center of Henan Province, Henan Institute of Technology, Xinxiang 453003, China
Abstract
To enhance fault characteristics and improve fault detection accuracy in bearing vibration signals, this paper proposes a fault diagnosis method using a wavelet packet energy spectrum and an improved deep confidence network. Firstly, a wavelet packet transform decomposes the original vibration signal into different frequency bands, fully preserving the original signal’s frequency information, and constructs feature vectors by extracting the energy of sub-frequency bands via the energy spectrum to extract and enhance fault feature information. Secondly, to minimize the time-consuming manual parameter adjustment procedure and increase the diagnostic accuracy, the sparrow search algorithm–deep belief network method is proposed, which utilizes the sparrow search algorithm to optimize the hyperparameters of the deep belief networks and reduce the classification error rate. Finally, to verify the effectiveness of the method, the rolling bearing data from Casey Reserve University were selected for verification, and compared to other commonly used algorithms, the proposed method achieved 100% and 99.34% accuracy in two sets of comparative experiments. The experimental results demonstrate that this method has a high diagnostic rate and stability.
Funder
Key Scientific and Technological Project of Henan Province
Industry–University Cooperative Education Program of the Ministry of Education
Key Scientific Research Projects of the Higher Education Institutions of Henan Province
Doctoral Fund of Henan Institute of Technology
Subject
Process Chemistry and Technology,Chemical Engineering (miscellaneous),Bioengineering
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