Detecting Internal Leakage in Electro-Hydraulic Actuators Based on Operational States of Motor

doi:10.3969/j.issn.1004-132X.2023.12.003

China Mechanical Engineering ›› 2023, Vol. 34 ›› Issue (12): 1407-1414.DOI: 10.3969/j.issn.1004-132X.2023.12.003

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Detecting Internal Leakage in Electro-Hydraulic Actuators Based on Operational States of Motor

HE Qingchuan1;LIU Hui2;PAN Jun1;CHEN Wenhua1

1.Zhejiang Provinces Key Laboratory of Reliability Technology for Mechanical and Electrical
Product,Zhejiang Sci-Tech University,Hangzhou，310018
2.Beijing Institute of Precision Mechatronics and Controls,Beijing，100076

Online:2023-06-25 Published:2023-07-12

基于电机运行状态的电静压伺服机构内泄漏检测方法

贺青川1;刘慧2;潘骏1;陈文华1

1.浙江理工大学浙江省机电产品可靠性技术研究重点实验室，杭州，310018
2.北京精密机电控制设备研究所，北京，100076

通讯作者: 陈文华（通信作者），男，1963年生，教授、博士研究生导师。研究方向为机械设计、可靠性试验分析、评估等。发表论文220余篇。E-mail:chenwh@zstu.edu.cn。
作者简介:贺青川，男，1984年生，讲师、博士。研究方向为机电产品可靠性试验评估、故障预测与健康管理等。发表论文20余篇。E-mail:heqingchuan@zstu.edu.cn。
基金资助:
装备预先研究领域基金（80902010302）；国家自然科学基金（51875529）；浙江省科技创新领军人才项目（2021R52036）

Abstract

Abstract: Internal leakage in an electro-hydraulic actuator（EHA） could not be identified by observation and also there were no effective methods for online detecting by using operational data. According to the relationship between state parameters of motor and leakage in closed hydraulic system, a detecting method of internal leakage by using motor current and speed signals was proposed. Based on deep learning algorithm, a convolution neural network was proposed which could be used to extract targeting weak features from monitoring data. A fault injection experiment was designed and the results show that the detection accuracy of internal leakage reaches 98.7% by using the proposed method, which provides an effective method for internal leakage detection in EHAs.

Key words: electro-hydraulic actuator(EHA), internal leakage, fault detection, hydraulic system, convolutional neural networks

摘要： 针对电静压伺服机构存在的内泄漏难以直接观测且缺乏有效间接检测方法的问题，利用电机运行状态变化与闭式循环液压系统内泄漏之间的关联关系，提出了一种基于电机电流、转速数据的电静压伺服机构内泄漏检测方法。为从运行状态数据中精准提取微弱故障特征，基于深度学习建立了一种能够定向提取数据特征的卷积神经网络算法。经故障植入实验验证，所提方法的内泄漏检测的准确率高达98.7%，从而为解决电静压伺服机构内泄漏检测难题提供了有效方法。

关键词: 电静压伺服机构, 内泄漏, 故障检测, 液压系统, 卷积神经网络

CLC Number:

TP137

HE Qingchuan, LIU Hui, PAN Jun, CHEN Wenhua. Detecting Internal Leakage in Electro-Hydraulic Actuators Based on Operational States of Motor[J]. China Mechanical Engineering, 2023, 34(12): 1407-1414.

贺青川, 刘慧, 潘骏, 陈文华. 基于电机运行状态的电静压伺服机构内泄漏检测方法[J]. 中国机械工程, 2023, 34(12): 1407-1414.

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Detecting Internal Leakage in Electro-Hydraulic Actuators Based on Operational States of Motor

基于电机运行状态的电静压伺服机构内泄漏检测方法

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