Gear Fault Diagnosis Based on EMD Decomposition and Levy-SSA-BP Neural Network
To solve the problem of early diagnosis of gear wear, this study proposes a fault diagnosis method based on empirical mode decomposition (EMD) and algorithm optimization of the back propagation (BP) neural network. Fristly, EMD is used to decompose acoustic emission signals, obtaining a series of i...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Journal of Mechanical Transmission
2024-05-01
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| Series: | Jixie chuandong |
| Subjects: | |
| Online Access: | http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2024.05.021 |
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| Summary: | To solve the problem of early diagnosis of gear wear, this study proposes a fault diagnosis method based on empirical mode decomposition (EMD) and algorithm optimization of the back propagation (BP) neural network. Fristly, EMD is used to decompose acoustic emission signals, obtaining a series of intrinsic mode function (IMF). Secondly, calculating the correlation coefficient of each IMF with the original signal, and the feature extractions of each component are carried out to form a feature matrix. Finally, the feature matrix is put into the BP neural network optimized by Levy flight and the sparrow search algorithm for identification. Comparing the BP neural network and the neural network optimized by the sparrow search algorithm, the algorithm proposed in this study has a higher accuracy rate, and the ability to identify minor wear faults is better, which can be used in early gear fault diagnosis. |
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| ISSN: | 1004-2539 |