Fault Diagnosis of Hydraulic Proportional Servo Valve Based on Time-Frequency Feature Extraction and GOA-SVM

Jiatong Li; Xingjian Wang; Yuwei Zhang; Runzhi Zhang; Yu Niu

doi:10.1109/ICIEA58696.2023.10241909

Fault Diagnosis of Hydraulic Proportional Servo Valve Based on Time-Frequency Feature Extraction and GOA-SVM

Jiatong Li
, Xingjian Wang^*
, Yuwei Zhang
, Runzhi Zhang
, Yu Niu

^*Corresponding author for this work

School of Automation Science and Electrical Engineering

Tianmushan Laboratory
School of Automation Science and Electrical Engineering
Beihang University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

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.

Original language	English
Title of host publication	Proceedings of the 18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023
Editors	Wenjian Cai, Guilin Yang, Jun Qiu, Tingting Gao, Lijun Jiang, Tianjiang Zheng, Xinli Wang
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1736-1741
Number of pages	6
ISBN (Electronic)	9798350312201
DOIs	https://doi.org/10.1109/ICIEA58696.2023.10241909
State	Published - 2023
Event	18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023 - Ningbo, China Duration: 18 Aug 2023 → 22 Aug 2023

Publication series

Name	Proceedings of the 18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023

Conference

Conference	18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023
Country/Territory	China
City	Ningbo
Period	18/08/23 → 22/08/23

Keywords

fault diagnosis
grasshopper optimization algorithm
hydraulic proportional servo valve
support vector machine

Access to Document

10.1109/ICIEA58696.2023.10241909

Cite this

Li, J., Wang, X., Zhang, Y., Zhang, R., & Niu, Y. (2023). Fault Diagnosis of Hydraulic Proportional Servo Valve Based on Time-Frequency Feature Extraction and GOA-SVM. In W. Cai, G. Yang, J. Qiu, T. Gao, L. Jiang, T. Zheng, & X. Wang (Eds.), Proceedings of the 18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023 (pp. 1736-1741). (Proceedings of the 18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICIEA58696.2023.10241909

Li, Jiatong ; Wang, Xingjian ; Zhang, Yuwei et al. / Fault Diagnosis of Hydraulic Proportional Servo Valve Based on Time-Frequency Feature Extraction and GOA-SVM. Proceedings of the 18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023. editor / Wenjian Cai ; Guilin Yang ; Jun Qiu ; Tingting Gao ; Lijun Jiang ; Tianjiang Zheng ; Xinli Wang. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 1736-1741 (Proceedings of the 18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023).

@inproceedings{2c55225cf8bd4e388ef996965ba9ab38,

title = "Fault Diagnosis of Hydraulic Proportional Servo Valve Based on Time-Frequency Feature Extraction and GOA-SVM",

abstract = "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.",

keywords = "fault diagnosis, grasshopper optimization algorithm, hydraulic proportional servo valve, support vector machine",

author = "Jiatong Li and Xingjian Wang and Yuwei Zhang and Runzhi Zhang and Yu Niu",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023 ; Conference date: 18-08-2023 Through 22-08-2023",

year = "2023",

doi = "10.1109/ICIEA58696.2023.10241909",

language = "英语",

series = "Proceedings of the 18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1736--1741",

editor = "Wenjian Cai and Guilin Yang and Jun Qiu and Tingting Gao and Lijun Jiang and Tianjiang Zheng and Xinli Wang",

booktitle = "Proceedings of the 18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023",

address = "美国",

}

Li, J, Wang, X , Zhang, Y, Zhang, R & Niu, Y 2023, Fault Diagnosis of Hydraulic Proportional Servo Valve Based on Time-Frequency Feature Extraction and GOA-SVM. in W Cai, G Yang, J Qiu, T Gao, L Jiang, T Zheng & X Wang (eds), Proceedings of the 18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023. Proceedings of the 18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023, Institute of Electrical and Electronics Engineers Inc., pp. 1736-1741, 18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023, Ningbo, China, 18/08/23. https://doi.org/10.1109/ICIEA58696.2023.10241909

Fault Diagnosis of Hydraulic Proportional Servo Valve Based on Time-Frequency Feature Extraction and GOA-SVM. / Li, Jiatong; Wang, Xingjian ; Zhang, Yuwei et al.
Proceedings of the 18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023. ed. / Wenjian Cai; Guilin Yang; Jun Qiu; Tingting Gao; Lijun Jiang; Tianjiang Zheng; Xinli Wang. Institute of Electrical and Electronics Engineers Inc., 2023. p. 1736-1741 (Proceedings of the 18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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

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 -

Li J, Wang X , Zhang Y, Zhang R, Niu Y. Fault Diagnosis of Hydraulic Proportional Servo Valve Based on Time-Frequency Feature Extraction and GOA-SVM. In Cai W, Yang G, Qiu J, Gao T, Jiang L, Zheng T, Wang X, editors, Proceedings of the 18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 1736-1741. (Proceedings of the 18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023). doi: 10.1109/ICIEA58696.2023.10241909

Fault Diagnosis of Hydraulic Proportional Servo Valve Based on Time-Frequency Feature Extraction and GOA-SVM

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