基于多角度特征提取的舵机故障诊断方法  

Method of Actuator Fault Diagnosis via Multiple Angles Feature Extraction

作　　者：王娜[1,2] 李杨 彭锟 WANG Na;LI Yang;PENG Kun(School of Control Science and Engineering,Tiangong University,Tianjin 300387,China;Tianjin Key Laboratory of Intelligent Control of Electrical Equipment,Tianjin 300387,China)

机构地区：[1]天津工业大学控制科学与工程学院,天津300387 [2]天津市电气装备智能控制重点实验室,天津300387

出　　处：《东北大学学报（自然科学版）》2022年第9期1240-1249,共10页Journal of Northeastern University(Natural Science)

基　　金：国家自然科学基金面上项目(61773279);天津市重点研发计划项目(19YFHBQY00040);天津大学微光机电系统技术教育部重点实验室开放基金资助项目(MOMST2016-4)。

摘　　要：针对舵机故障中的抖动问题,提出一种基于多角度特征提取的故障诊断方法.利用短时分析法分帧舵机数据,以获得短时平稳的时间序列;引入能熵比概念提取舵机数据帧内的电流特征,并利用动态时间规整思想提取舵机数据帧内的位置特征,形成多角度特征以增强输入特征的显著性.在此基础上,利用双向长短时记忆网络提高后续舵机故障分类过程的准确性.通过某型舵机抖动实测数据的仿真,并与传统长短时记忆网络的故障诊断结果比较,验证了所提方法的有效性.For the swaying problem of actuator faults,a fault diagnosis method based on multi-angle feature extraction is proposed.The short-time analysis idea is used to frame the actuator data to obtain a short-stable time series.The energy-entropy-ratio concept is introduced to extract the current features of actuator datum frame.Dynamic time-regular thoughts to extract positional characteristics within the actuator data frame is utilized.A multi-angle feature to enhance the significance of input characteristics is formed.On this basis,the bidirectional long-short term memory network is used to improve the accuracy of the subsequent actuator fault classification process.Finally,by the measured data from a certain type actuator swaying,the validity of the proposed method is verified by the comparing with the traditional short term memory network methods.

关 键 词：舵机故障诊断 特征提取 短时分析 能熵比 动态时间规整 双向长短时记忆网络 

分 类 号：TP206[自动化与计算机技术—检测技术与自动化装置]