数据驱动的非线性系统自适应故障诊断方法  

Data-driven adaptive fault diagnosis method for nonlinear systems

作　　者：郭凯谱 李红飞 范玲玲 吉鸿海 GUO Kaipu;LI Hongfei;FAN Lingling;JI Honghai(School of Automation,Beijing Information Science and Technology University,Beijing 100192,China;National Industrial Information Security Development And Research Center,Beijing 100040,China;School of Electrical and Control Engineering,North China University of Technology,Beijing 100093,China)

机构地区：[1]北京信息科技大学自动化学院,北京100192 [2]国家工业信息安全发展研究中心,北京100040 [3]北方工业大学电气与控制工程学院,北京100093

出　　处：《河南理工大学学报（自然科学版）》2023年第6期134-141,共8页Journal of Henan Polytechnic University（Natural Science）

基　　金：国家自然科学基金资助项目(6170020910)。

摘　　要：针对一类离散时间非线性系统,基于数据驱动自适应滤波故障诊断(data-driven adaptive filtering for fault diagnosis,DDAF-FD)方法,实现对执行器和传感器故障的同时在线估计。首先采用动态线性化技术,将非线性系统等价转化为类线性模型,解决非线性系统难以精确建模的问题;其次,仅利用系统I/O数据,在数据驱动滤波和递归最小二乘算法的框架下,设计一种数据驱动自适应故障诊断方法,实现对两种故障失效因子的实时准确估计;最后,利用Lyapunov方法验证所提方法的稳定性,并利用对比仿真实验验证了该方法的有效性。For a class of discrete time nonlinear systems,the simultaneous online estimation of actuator and sensor faults was realized based on the data-driven adaptive filtering for fault diagnosis(DDAF-FD)method.Firstly,the dynamic linearization technique was used to transform the nonlinear system into a quasi-linear model,which solved the problem that the nonlinear system was difficult to model accurately.Secondly,only using system I/O data,a data-driven adaptive fault diagnosis method was designed under the framework of data-driven filtering and recursive least squares algorithm,and the real-time accurate estimation of the two fault failure factors was realized.The stability of the proposed method was proved by Lyapunov method.The effectiveness of the proposed method was verified by the comparison of simulation experiment.

关 键 词：数据驱动滤波 动态线性化 故障诊断 最小二乘法 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]