GUO K P, LI H F, FAN L L,et al.Data-driven adaptive fault diagnosis method for nonlinear systems[J].Journal of Henan Polytechnic University(Natural Science) ,2023,42(6):134-141.

doi:10.16186/j.cnki.1673-9787.2021120122

Received:2021/12/30

Revised:2022/04/29

Published:2023/11/25

Data-driven adaptive fault diagnosis method for nonlinear systems

GUO Kaipu1, LI Hongfei2, FAN Lingling1, JI Honghai3

1.School of Automation，Beijing Information Science and Technology University，Beijing  100192，China；2.National Industrial Information Security Development And Research Center，Beijing  100040，China；3.School of Electrical and Control Engineering，North China University of Technology，Beijing  100093，China

Abstract: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.

Key words:data-driven filtering;dynamic linearization;fault diagnosis;least squares algorithm

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