Neurofuzzy Approach to Fault Detection of Nonlinear Systems

This paper proposes a neurofuzzy approach to fault detection in linear systems. The system diagnosed is described by using a neurofuzzy model called LimNet that consists of a linear model and multiple local linear models with interpolation of a "fuzzy basis function". Fault detection is considered in two cases: when faults occur in the linear model part, a KDI-based robust fault detection is applied, where a multi-local-model part is treated as error due to nonlinear undermodeling; when faults occur in the multi-local-model part, a multi-model based fault detection method is developed, in which the identified LimNet is interpreted as several local ARMAX models, and KDI is used as an index to discriminate between each local model and its reference. This paper mainly concentrates discussions on multi-model based fault detection.

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A SOS-based robust fault detection method for polynomial nonlinear systems

Proceedings of the 2010 American Control Conference ◽

10.1109/acc.2010.5531310 ◽

2010 ◽

Author(s):

A Casavola ◽

D Famularo ◽

G Franze

Keyword(s):

Nonlinear Systems ◽

Fault Detection ◽

Detection Method ◽

Robust Fault Detection

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A stochastic Functional Model based method for random vibration based robust fault detection under variable non–measurable operating conditions with application to railway vehicle suspensions

Journal of Sound and Vibration ◽

10.1016/j.jsv.2019.115006 ◽

2020 ◽

Vol 466 ◽

pp. 115006 ◽

Cited By ~ 2

Author(s):

T.-C.I. Aravanis ◽

J.S. Sakellariou ◽

S.D. Fassois

Keyword(s):

Fault Detection ◽

Random Vibration ◽

Functional Model ◽

Operating Conditions ◽

Railway Vehicle ◽

Vehicle Suspensions ◽

Model Based ◽

Robust Fault Detection ◽

Stochastic Functional

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Model-based fault detection filter for Markovian jump linear systems applied to a control moment gyroscope

European Journal of Control ◽

10.1016/j.ejcon.2021.02.003 ◽

2021 ◽

Vol 59 ◽

pp. 99-108

Author(s):

Leonardo de Paula Carvalho ◽

Fabio Yukio Toriumi ◽

Bruno Augusto Angélico ◽

Oswaldo Luiz do Valle Costa

Keyword(s):

Fault Detection ◽

Linear Systems ◽

Markovian Jump ◽

Jump Linear Systems ◽

Control Moment Gyroscope ◽

Fault Detection Filter ◽

Model Based ◽

Control Moment ◽

Markovian Jump Linear Systems ◽

Detection Filter

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Hybrid bond graph model based for robust fault detection and isolation

Proceedings of the Institution of Mechanical Engineers Part I Journal of Systems and Control Engineering ◽

10.1177/0959651815616026 ◽

2015 ◽

Vol 230 (2) ◽

pp. 145-163 ◽

Cited By ~ 4

Author(s):

Mohamed Ilyas Rahal ◽

Belkacem Ould Bouamama ◽

Abdelmajid Meghebbar

Keyword(s):

Fault Detection ◽

Graph Model ◽

Bond Graph ◽

Fault Detection And Isolation ◽

Model Based ◽

Robust Fault Detection

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A robust fault detection filter for polynomial nonlinear systems via sum-of-squares decompositions

Systems & Control Letters ◽

10.1016/j.sysconle.2012.05.001 ◽

2012 ◽

Vol 61 (8) ◽

pp. 839-848 ◽

Cited By ~ 15

Author(s):

Giuseppe Franzè ◽

Domenico Famularo

Keyword(s):

Nonlinear Systems ◽

Fault Detection ◽

Sum Of Squares ◽

Fault Detection Filter ◽

Robust Fault Detection ◽

Detection Filter

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Robust fault detection using iterative learning observer for nonlinear systems

Fifth World Congress on Intelligent Control and Automation (IEEE Cat. No.04EX788) ◽

10.1109/wcica.2004.1340967 ◽

2004 ◽

Author(s):

Ma Liling ◽

Wang Junzheng ◽

Wang Shoukun

Keyword(s):

Nonlinear Systems ◽

Fault Detection ◽

Iterative Learning ◽

Robust Fault Detection

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KDI-Based Robust Fault Detection Scheme for Nearly Linear Systems

Proceedings of the ISCIE International Symposium on Stochastic Systems Theory and its Applications ◽

10.5687/sss.1998.13 ◽

1998 ◽

Vol 1998 (0) ◽

pp. 13-18 ◽

Cited By ~ 1

Author(s):

Jinglu HU ◽

Kousuke KUMAMARU ◽

Katsuhiro INOUE ◽

Kotaro HIRASAWA

Keyword(s):

Fault Detection ◽

Linear Systems ◽

Detection Scheme ◽

Robust Fault Detection

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Robust fault detection in descriptor linear systems via generalized unknown input observers

International Journal of Systems Science ◽

10.1080/00207720210123715 ◽

2002 ◽

Vol 33 (5) ◽

pp. 369-377 ◽

Cited By ~ 51

Author(s):

G. R. Duan ◽

D. Howe ◽

R. J. Patton

Keyword(s):

Fault Detection ◽

Linear Systems ◽

Unknown Input ◽

Robust Fault Detection ◽

Unknown Input Observers ◽

Descriptor Linear Systems

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Application of Residual-Based EWMA Control Charts for Detecting Faults in Variable-Air-Volume Air Handling Unit System

Journal of Control Science and Engineering ◽

10.1155/2016/1467823 ◽

2016 ◽

Vol 2016 ◽

pp. 1-7 ◽

Cited By ~ 1

Author(s):

Haitao Wang

Keyword(s):

Time Series ◽

Fault Detection ◽

Control Chart ◽

Detection Method ◽

Time Data ◽

Air Handling Unit ◽

Ewma Control Chart ◽

Robust Fault Detection ◽

Modeling Errors ◽

Control Limits

An online robust fault detection method is presented in this paper for VAV air handling unit and its implementation. Residual-based EWMA control chart is used to monitor the control processes of air handling unit and detect faults of air handling unit. In order to provide a level of robustness with respect to modeling errors, control limits are determined by incorporating time series model uncertainty in EWMA control chart. The fault detection method proposed was tested and validated using real time data collected from real VAV air-conditioning systems involving multiple artificial faults. The results of validation show residual-based EWMA control chart with designing control limits can improve the accuracy of fault detection through eliminating the negative effects of dynamic characteristics, serial correlation, normal transient changes of system, and time series modeling errors. The robust fault detection method proposed can provide an effective tool for detecting the faults of air handling units.

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