Hydraulic pump fault diagnosis based on improved empirical wavelet transform and kernel extreme learning machine

Chao Chen; Kelimu Muhetae; Jing Wei

doi:10.1117/12.2689750

7 September 2023 Hydraulic pump fault diagnosis based on improved empirical wavelet transform and kernel extreme learning machine

Chao Chen, Kelimu Muhetae, Jing Wei

Author Affiliations +

Proceedings Volume 12790, Eighth International Conference on Electromechanical Control Technology and Transportation (ICECTT 2023); 1279050 (2023) https://doi.org/10.1117/12.2689750
Event: 8th International Conference on Electromechanical Control Technology and Transportation (ICECTT 2023), 2023, Hangzhou, China

Abstract

Aiming at the problem of the high failure rate of hydraulic pumps of agricultural machinery and the lack of effective means and methods for fault determination, this paper proposes a hydraulic pump fault diagnosis method based on empirical wavelet transform (EWT) and kernel limit learning machine for solving the diagnosis of hydraulic pump faults in agricultural machinery. Firstly, k-means is used to improve EWT, which makes the signal decomposition more accurate. Then, the decomposed sub-signals are fed into the seagull optimization algorithm (SOA) improved kernel limit learning machine for fault classification. From the experimental results, it is known that the proposed method has an accuracy of 97.67% for hydraulic pump fault diagnosis and can effectively diagnose the faults of hydraulic pumps of agricultural machinery.

Citation Download Citation

Chao Chen, Kelimu Muhetae, and Jing Wei "Hydraulic pump fault diagnosis based on improved empirical wavelet transform and kernel extreme learning machine", Proc. SPIE 12790, Eighth International Conference on Electromechanical Control Technology and Transportation (ICECTT 2023), 1279050 (7 September 2023); https://doi.org/10.1117/12.2689750

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