Data-Driven Kalman Filtering in Nonlinear Systems with Actuator and Sensor Fault Diagnosis Based on Lyapunov Stability

This study proposes a data-driven adaptive filtering method for the fault diagnosis (DDAF-FD) of discrete-time nonlinear systems and provides a simultaneous online estimation of actuator and sensor faults. First, dynamic linearization was adopted to transform the nonlinear system into a quasi-linear...

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Autores principales:	Lingling Fan, Kaipu Guo, Honghai Ji, Shida Liu, Yuzhou Wei
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	data-driven filtering dynamic linearization fault diagnosis recursive least-squares Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/f7112f50248744bc86042a8e5cbd84af
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id	oai:doaj.org-article:f7112f50248744bc86042a8e5cbd84af
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spelling	oai:doaj.org-article:f7112f50248744bc86042a8e5cbd84af2021-11-25T19:06:19ZData-Driven Kalman Filtering in Nonlinear Systems with Actuator and Sensor Fault Diagnosis Based on Lyapunov Stability10.3390/sym131120472073-8994https://doaj.org/article/f7112f50248744bc86042a8e5cbd84af2021-10-01T00:00:00Zhttps://www.mdpi.com/2073-8994/13/11/2047https://doaj.org/toc/2073-8994This study proposes a data-driven adaptive filtering method for the fault diagnosis (DDAF-FD) of discrete-time nonlinear systems and provides a simultaneous online estimation of actuator and sensor faults. First, dynamic linearization was adopted to transform the nonlinear system into a quasi-linear model, which facilitated accurate modeling of the nonlinear system. Second, a data-driven adaptive fault diagnosis method was designed under the framework of data-driven filtering and the recursive least-squares algorithm using system I/O data only, and accurate real-time estimation of two fault factors was achieved. In addition, the simulation results demonstrate the effectiveness of the proposed method. The stability was verified via the Lyapunov method.Lingling FanKaipu GuoHonghai JiShida LiuYuzhou WeiMDPI AGarticledata-driven filteringdynamic linearizationfault diagnosisrecursive least-squaresMathematicsQA1-939ENSymmetry, Vol 13, Iss 2047, p 2047 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	data-driven filtering dynamic linearization fault diagnosis recursive least-squares Mathematics QA1-939
spellingShingle	data-driven filtering dynamic linearization fault diagnosis recursive least-squares Mathematics QA1-939 Lingling Fan Kaipu Guo Honghai Ji Shida Liu Yuzhou Wei Data-Driven Kalman Filtering in Nonlinear Systems with Actuator and Sensor Fault Diagnosis Based on Lyapunov Stability
description	This study proposes a data-driven adaptive filtering method for the fault diagnosis (DDAF-FD) of discrete-time nonlinear systems and provides a simultaneous online estimation of actuator and sensor faults. First, dynamic linearization was adopted to transform the nonlinear system into a quasi-linear model, which facilitated accurate modeling of the nonlinear system. Second, a data-driven adaptive fault diagnosis method was designed under the framework of data-driven filtering and the recursive least-squares algorithm using system I/O data only, and accurate real-time estimation of two fault factors was achieved. In addition, the simulation results demonstrate the effectiveness of the proposed method. The stability was verified via the Lyapunov method.
format	article
author	Lingling Fan Kaipu Guo Honghai Ji Shida Liu Yuzhou Wei
author_facet	Lingling Fan Kaipu Guo Honghai Ji Shida Liu Yuzhou Wei
author_sort	Lingling Fan
title	Data-Driven Kalman Filtering in Nonlinear Systems with Actuator and Sensor Fault Diagnosis Based on Lyapunov Stability
title_short	Data-Driven Kalman Filtering in Nonlinear Systems with Actuator and Sensor Fault Diagnosis Based on Lyapunov Stability
title_full	Data-Driven Kalman Filtering in Nonlinear Systems with Actuator and Sensor Fault Diagnosis Based on Lyapunov Stability
title_fullStr	Data-Driven Kalman Filtering in Nonlinear Systems with Actuator and Sensor Fault Diagnosis Based on Lyapunov Stability
title_full_unstemmed	Data-Driven Kalman Filtering in Nonlinear Systems with Actuator and Sensor Fault Diagnosis Based on Lyapunov Stability
title_sort	data-driven kalman filtering in nonlinear systems with actuator and sensor fault diagnosis based on lyapunov stability
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/f7112f50248744bc86042a8e5cbd84af
work_keys_str_mv	AT linglingfan datadrivenkalmanfilteringinnonlinearsystemswithactuatorandsensorfaultdiagnosisbasedonlyapunovstability AT kaipuguo datadrivenkalmanfilteringinnonlinearsystemswithactuatorandsensorfaultdiagnosisbasedonlyapunovstability AT honghaiji datadrivenkalmanfilteringinnonlinearsystemswithactuatorandsensorfaultdiagnosisbasedonlyapunovstability AT shidaliu datadrivenkalmanfilteringinnonlinearsystemswithactuatorandsensorfaultdiagnosisbasedonlyapunovstability AT yuzhouwei datadrivenkalmanfilteringinnonlinearsystemswithactuatorandsensorfaultdiagnosisbasedonlyapunovstability
_version_	1718410316808716288

Data-Driven Kalman Filtering in Nonlinear Systems with Actuator and Sensor Fault Diagnosis Based on Lyapunov Stability

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