An intelligent fault diagnosis method for an electromechanical actuator based on sparse feature and long short-term network

作     者：Yang, Jing Guo, Yingqing Zhao, Wanli 

作者机构：Northwestern Polytech Univ Sch Power & Energy Xian 710072 Peoples R China 

出 版 物：《MEASUREMENT SCIENCE AND TECHNOLOGY》 (测量科学与技术)

年 卷 期：2021年第32卷第9期

页      面：095102-095102页

核心收录：

学科分类：08[工学] 080401[工学-精密仪器及机械] 0804[工学-仪器科学与技术] 081102[工学-检测技术与自动化装置] 0811[工学-控制科学与工程] 

主　　题：electro-mechanical actuator sparse auto-encoder long-short memory network sparse feature fault diagnosis 

摘      要：Electromechanical actuators (EMAs), as the new generation of actuators, have an important impact on the safety of aircraft. With the development of measurement technology, a large amount of data provides a broad prospect for the data-based fault diagnosis method. However, the existence of redundant data increases the burden of software and hardware. Therefore, a semi-supervised sparse auto-encoder (SSAE) is employed to prune observed data based on sparsity analysis. Moreover, temporal and spatial relationships are explored by a multi-channel long short-term network to build a time series model, so as to perform fault detection and isolation based on the difference between its estimated and observed values. Due to its sparse feature extraction capability, the SSAE can improve the fault isolation accuracy while pruning observed data. Verification results confirm that the proposed method can effectively diagnose EMA faults.