Fault detection and diagnosis for nonlinear systems: A new adaptive Gaussian mixture modeling approach

Modeling Approach ◽

Gaussian Mixture Modeling ◽

Detection And Diagnosis ◽

Mixture Modeling Approach

Gaussian mixture modeling approach for stationary human identification in through-the-wall radar imagery

Journal of Electronic Imaging ◽

10.1117/1.jei.24.1.013028 ◽

2015 ◽

Vol 24 (1) ◽

pp. 013028 ◽

Cited By ~ 6

Author(s):

Vamsi Kilaru ◽

Moeness Amin ◽

Fauzia Ahmad ◽

Pascale Sévigny ◽

David DiFilippo

Keyword(s):

Human Identification ◽

Gaussian Mixture ◽

Mixture Modeling ◽

Modeling Approach ◽

Gaussian Mixture Modeling ◽

Radar Imagery ◽

Mixture Modeling Approach

A Hidden Markov Model application with Gaussian Mixture emissions for fault detection and diagnosis on a simulated AUV platform

OCEANS 2016 MTS/IEEE Monterey ◽

10.1109/oceans.2016.7761280 ◽

2016 ◽

Author(s):

Mariela De Lucas Alvarez ◽

David M. Lane

Keyword(s):

Fault Detection ◽

Markov Model ◽

Hidden Markov Model ◽

Hidden Markov ◽

Gaussian Mixture ◽

Model Application ◽

An adaptive observer-based fault detection and diagnosis for nonlinear systems with sensor and actuator faults

2015 International Conference on Advanced Mechatronic Systems (ICAMechS) ◽

10.1109/icamechs.2015.7287160 ◽

2015 ◽

Author(s):

Xiaomo Yan ◽

Bailing Tian ◽

Hong Wang

Keyword(s):

Nonlinear Systems ◽

Fault Detection ◽

Adaptive Observer ◽

Actuator Faults ◽

Integrated State Estimation, Fault Detection and Diagnosis for Nonlinear Systems

1993 American Control Conference ◽

10.23919/acc.1993.4792881 ◽

1993 ◽

Author(s):

Douglas Robertson ◽

Jay H. Lee

Keyword(s):

Nonlinear Systems ◽

Fault Detection ◽

State Estimation ◽

Fault Detection and Diagnosis for a Class of Nonlinear Systems with Decentralized Event-triggered Transmissions ★ ★This work was supported by the National Natural Science Foundation of China under Grants 61490701, 61210012, 61290324, 61473163, and 61273156, Tsinghua University Initiative Scientific Research Program, and Jiangsu Provincial Key Laboratory of E-business at Nanjing University of Finance and Economics of China under Grant JSEB201301.

IFAC-PapersOnLine ◽

10.1016/j.ifacol.2015.09.679 ◽

2015 ◽

Vol 48 (21) ◽

pp. 1134-1139 ◽

Author(s):

LIU Yang ◽

HE Xiao ◽

WANG Zidong ◽

ZHOU Dong-Hua

Keyword(s):

Nonlinear Systems ◽

Fault Detection ◽

Research Program ◽

Natural Science ◽

Scientific Research ◽

Tsinghua University ◽

Detection And Diagnosis ◽

Scientific Research Program ◽

Event Triggered

Proceedings of the 4th World Congress on Intelligent Control and Automation (Cat. No.02EX527) ◽

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

10.1109/wcica.2002.1020009 ◽

2003 ◽

Cited By ~ 1

Author(s):

Liling Ma ◽

Yinghua Yang ◽

Fuli Wang ◽

Ningyun Lu

Keyword(s):

Nonlinear Systems ◽

Fault Detection ◽

Sliding Mode ◽

Sliding Mode Observer ◽

Uncertain Nonlinear Systems ◽

Fault Detection and Diagnosis for Nonlinear Systems: A Support Vector Machine Approach

IFAC Proceedings Volumes ◽

10.3182/20090921-3-tr-3005.00063 ◽

2009 ◽

Vol 42 (19) ◽

pp. 355-360 ◽

Cited By ~ 3

Author(s):

Rana Ortaç-Kabaoğlu ◽

İbrahim Eksin ◽

Engin Yeşil ◽

Müjde Güzelkaya

Keyword(s):

Support Vector Machine ◽

Nonlinear Systems ◽

Fault Detection ◽

Support Vector ◽

Proceedings of the 2000 American Control Conference. ACC (IEEE Cat. No.00CH36334) ◽

A variable structure adaptive observer approach for actuator fault detection and diagnosis in uncertain nonlinear systems

10.1109/acc.2000.878691 ◽

2000 ◽

Cited By ~ 12

Author(s):

Wen Chen ◽

M. Saif ◽

Yeng-Chai Soh

Keyword(s):

Nonlinear Systems ◽

Fault Detection ◽

Variable Structure ◽

Adaptive Observer ◽

Actuator Fault ◽

Uncertain Nonlinear Systems ◽

Adaptive fault detection and diagnosis using parsimonious Gaussian mixture models trained with distributed computing techniques

Journal of the Franklin Institute ◽

10.1016/j.jfranklin.2016.11.024 ◽

2017 ◽

Vol 354 (6) ◽

pp. 2543-2572 ◽

Cited By ~ 6

Author(s):

Thiago A. Nakamura ◽

Reinaldo M. Palhares ◽

Walmir M. Caminhas ◽

Benjamin R. Menezes ◽

Mário Cesar M.M. de Campos ◽

...

Keyword(s):

Fault Detection ◽

Distributed Computing ◽

Mixture Models ◽

Gaussian Mixture Models ◽

Gaussian Mixture ◽

A nonlinear kernel Gaussian mixture model based inferential monitoring approach for fault detection and diagnosis of chemical processes

Chemical Engineering Science ◽

10.1016/j.ces.2011.10.011 ◽

2012 ◽

Vol 68 (1) ◽

pp. 506-519 ◽

Cited By ~ 120

Author(s):

Jie Yu

Keyword(s):

Fault Detection ◽

Gaussian Mixture Model ◽

Mixture Model ◽

Gaussian Mixture ◽

Chemical Processes ◽

Model Based ◽