基于多级观测器的液压伺服系统自适应故障检测与隔离  

An Adaptive Multistage Observer in Detecting and Isolating Fault in Hydraulic Servo System

作　　者：沈新刚[1] 吕镇邦[1] 孙倩[1] SHEN Xingang;LYU Zhenbang;SUN Qian(Aeronautical Computing Technique Research Institute,AVIC,Xi’an 710068,China)

机构地区：[1]航空工业西安航空计算技术研究所,西安710068

出　　处：《空军工程大学学报（自然科学版）》2020年第5期36-42,共7页Journal of Air Force Engineering University(Natural Science Edition)

基　　金：国防基础科研计划项目资助(JCKY2016205A004)。

摘　　要：针对传统的基于固定阈值的故障检测及诊断方法虚警率高,无法有效实现液压伺服系统的故障检测与隔离,提出了一套基于多级观测器的液压伺服系统自适应故障检测与隔离方法。首先,采用第1级RBF网络作为液压伺服系统观测器,通过比较观测器估计输出值与实际系统输出得到残差信号。其次,采用第2级RBF神经网络产生自适应阈值,实现了液压伺服系统自适应故障检测。最后,采用小波包分析提取残差信号特征,利用第3级RBF神经网络实现系统的典型故障隔离。实验结果表明,利用多级观测器模型能够有效实现液压伺服系统的自适应故障检测及隔离。Aimed at the problems that the traditional fault detection and diagnosis methods based on fixed-threshold have a high false alarm rate and fail to realize effective fault detection and isolation,an adaptive fault detection and isolation method for hydraulic servo system based on multistage observer is presented.The first-stage RBF neural network is adopted as a fault observer of the hydraulic servo system,and the residual error signal is generated by comparing the estimated observer output with the actual measurements.The second-stage RBF neural network is employed as an adaptive threshold producer,realizing the adaptive fault detection.Features of the residual error signal are extracted by using wavelet packet analysis,and the system fault isolation is made by using the third-stage RBF neural network.The experimental results show that the multistage observer is effective in detecting and isolating the failure in the hydraulic servo system.

关 键 词：液压伺服系统 自适应故障检测 故障隔离 RBF神经网络 

分 类 号：TH17[机械工程—机械制造及自动化]