A sliding mode observer approach for fault detection and diagnosis in uncertain nonlinear systems

Liling Ma; Yinghua Yang; Fuli Wang; Ningyun Lu

A sliding mode observer approach for fault detection and diagnosis in uncertain nonlinear systems

Liling Ma^*, Yinghua Yang, Fuli Wang, Ningyun Lu

^*Corresponding author for this work

Northeastern University China

Research output: Contribution to conference › Paper › peer-review

4 Citations (Scopus)

Abstract

This paper presents a sliding mode observer approach of fault detection and diagnosis for nonlinear systems with uncertainty having unknown bounds. The robustness properties of the observer ensure that no false alarms are registered due to uncertainties and disturbances in the system. The observer uses nonlinear gains that are smoothened versions of classical sliding mode gains and they are continuously updated to guarantee a globally stable observation error. A neural network is designed to capture the nonlinear characteristics of faults. At last, simulation results have shown the feasibility and effectiveness of the method.

Original language	English
Pages	2714-2717
Number of pages	4
Publication status	Published - 2002
Externally published	Yes
Event	Proceedings of the 4th World Congress on Intelligent Control and Automation - Shanghai, China Duration: 10 Jun 2002 → 14 Jun 2002

Conference

Conference	Proceedings of the 4th World Congress on Intelligent Control and Automation
Country/Territory	China
City	Shanghai
Period	10/06/02 → 14/06/02

Keywords

Fault detection
Fault diagnosis
Neural network
Nonlinear system
Sliding mode observer

Cite this

@conference{2af17695b70141b98fecbc805df2b97c,

title = "A sliding mode observer approach for fault detection and diagnosis in uncertain nonlinear systems",

abstract = "This paper presents a sliding mode observer approach of fault detection and diagnosis for nonlinear systems with uncertainty having unknown bounds. The robustness properties of the observer ensure that no false alarms are registered due to uncertainties and disturbances in the system. The observer uses nonlinear gains that are smoothened versions of classical sliding mode gains and they are continuously updated to guarantee a globally stable observation error. A neural network is designed to capture the nonlinear characteristics of faults. At last, simulation results have shown the feasibility and effectiveness of the method.",

keywords = "Fault detection, Fault diagnosis, Neural network, Nonlinear system, Sliding mode observer",

author = "Liling Ma and Yinghua Yang and Fuli Wang and Ningyun Lu",

year = "2002",

language = "English",

pages = "2714--2717",

note = "Proceedings of the 4th World Congress on Intelligent Control and Automation ; Conference date: 10-06-2002 Through 14-06-2002",

}

TY - CONF

T1 - A sliding mode observer approach for fault detection and diagnosis in uncertain nonlinear systems

AU - Ma, Liling

AU - Yang, Yinghua

AU - Wang, Fuli

AU - Lu, Ningyun

PY - 2002

Y1 - 2002

N2 - This paper presents a sliding mode observer approach of fault detection and diagnosis for nonlinear systems with uncertainty having unknown bounds. The robustness properties of the observer ensure that no false alarms are registered due to uncertainties and disturbances in the system. The observer uses nonlinear gains that are smoothened versions of classical sliding mode gains and they are continuously updated to guarantee a globally stable observation error. A neural network is designed to capture the nonlinear characteristics of faults. At last, simulation results have shown the feasibility and effectiveness of the method.

AB - This paper presents a sliding mode observer approach of fault detection and diagnosis for nonlinear systems with uncertainty having unknown bounds. The robustness properties of the observer ensure that no false alarms are registered due to uncertainties and disturbances in the system. The observer uses nonlinear gains that are smoothened versions of classical sliding mode gains and they are continuously updated to guarantee a globally stable observation error. A neural network is designed to capture the nonlinear characteristics of faults. At last, simulation results have shown the feasibility and effectiveness of the method.

KW - Fault detection

KW - Fault diagnosis

KW - Neural network

KW - Nonlinear system

KW - Sliding mode observer

UR - http://www.scopus.com/inward/record.url?scp=0036955852&partnerID=8YFLogxK

M3 - Paper

AN - SCOPUS:0036955852

SP - 2714

EP - 2717

T2 - Proceedings of the 4th World Congress on Intelligent Control and Automation

Y2 - 10 June 2002 through 14 June 2002

ER -

A sliding mode observer approach for fault detection and diagnosis in uncertain nonlinear systems

Abstract

Conference

Keywords

Other files and links

Fingerprint

Cite this