东北大学学报(自然科学版) ›› 2022, Vol. 43 ›› Issue (9): 1240-1249.DOI: 10.12068/j.issn.1005-3026.2022.09.004

• 信息与控制 • 上一篇    下一篇

基于多角度特征提取的舵机故障诊断方法

王娜1,2, 李杨1, 彭锟1   

  1. (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)
  • 发布日期:2022-09-16
  • 通讯作者: 王娜
  • 作者简介:王娜(1977-),女,河北衡水人,天津工业大学讲师.
  • 基金资助:
    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).

Method of Actuator Fault Diagnosis via Multiple Angles Feature Extraction

WANG Na1,2, LI Yang1, PENG Kun1   

  1. 1. School of Control Science and Engineering, Tiangong University, Tianjin 300387, China; 2. Tianjin Key Laboratory of Intelligent Control of Electrical Equipment, Tianjin 300387, China.
  • Published:2022-09-16
  • Contact: WANG Na
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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.

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

Abstract: 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.

Key words: actuator fault diagnosis; feature extraction; short-time analysis; energy-entropy-ratio; dynamic time warping; bi-directional long-short term memory network

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