TY - GEN
T1 - Fault Diagnosis of Hydraulic Proportional Servo Valve Based on Time-Frequency Feature Extraction and GOA-SVM
AU - Li, Jiatong
AU - Wang, Xingjian
AU - Zhang, Yuwei
AU - Zhang, Runzhi
AU - Niu, Yu
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Aiming at difficulty in effective fault diagnosis in actual work, a new fault diagnosis method of the hydraulic proportional servo valve based on Grasshopper Optimized Support Vector Machine (GOA-SVM) is proposed. Firstly, the denoising method based on wavelet transform removes the random noise of spool position signal, pressure signal and current signal. Then, time-frequency feature extraction and construction of fault feature vectors are performed using time domain, frequency domain and energy entropy in time and frequency domain. Based on GOA-SVM, the valve fault pattern recognition is performed. The experimental results show that the pattern recognition accuracy of the method reaches more than 95% and can be used for the fault diagnosis of hydraulic proportional servo valves.
AB - Aiming at difficulty in effective fault diagnosis in actual work, a new fault diagnosis method of the hydraulic proportional servo valve based on Grasshopper Optimized Support Vector Machine (GOA-SVM) is proposed. Firstly, the denoising method based on wavelet transform removes the random noise of spool position signal, pressure signal and current signal. Then, time-frequency feature extraction and construction of fault feature vectors are performed using time domain, frequency domain and energy entropy in time and frequency domain. Based on GOA-SVM, the valve fault pattern recognition is performed. The experimental results show that the pattern recognition accuracy of the method reaches more than 95% and can be used for the fault diagnosis of hydraulic proportional servo valves.
KW - fault diagnosis
KW - grasshopper optimization algorithm
KW - hydraulic proportional servo valve
KW - support vector machine
UR - https://www.scopus.com/pages/publications/85173614089
U2 - 10.1109/ICIEA58696.2023.10241909
DO - 10.1109/ICIEA58696.2023.10241909
M3 - 会议稿件
AN - SCOPUS:85173614089
T3 - Proceedings of the 18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023
SP - 1736
EP - 1741
BT - Proceedings of the 18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023
A2 - Cai, Wenjian
A2 - Yang, Guilin
A2 - Qiu, Jun
A2 - Gao, Tingting
A2 - Jiang, Lijun
A2 - Zheng, Tianjiang
A2 - Wang, Xinli
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023
Y2 - 18 August 2023 through 22 August 2023
ER -