Robust fault diagnosis in a chemical process using multiple-model approach

Proceedings of the 40th IEEE Conference on Decision and Control (Cat. No.01CH37228) ◽

10.1109/cdc.2001.980089 ◽

2003 ◽

Author(s):

R.J. Patton ◽

C.J. Lopez Toribio ◽

S. Simani

Keyword(s):

Fault Diagnosis ◽

Chemical Process ◽

Multiple Model ◽

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Particle filtering based multiple-model approach to fault diagnosis in nonlinear stochastic systems

2001 European Control Conference (ECC) ◽

10.23919/ecc.2001.7076109 ◽

2001 ◽

Author(s):

Ping Li ◽

Visakan Kadirkamanathan

Keyword(s):

Fault Diagnosis ◽

Particle Filtering ◽

Stochastic Systems ◽

Multiple Model ◽

Nonlinear Stochastic Systems ◽

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Sensor Fault Diagnosis and Estimation Based on Multiple-Model Approach for Aeroengine

2018 IEEE CSAA Guidance, Navigation and Control Conference (CGNCC) ◽

10.1109/gncc42960.2018.9018924 ◽

2018 ◽

Author(s):

Wanli Zhao ◽

Yingqing Guo ◽

Chenyang Lai

Keyword(s):

Fault Diagnosis ◽

Multiple Model ◽

Sensor Fault ◽

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Fault Diagnosis on Industrial Systems Based on a Multiple Model Approach

IFAC Proceedings Volumes ◽

10.1016/s1474-6670(17)31008-x ◽

2004 ◽

Vol 37 (15) ◽

pp. 107-112

Author(s):

M. Rodrigues ◽

M. Adam-Medina ◽

D. Theilliol ◽

D. Sauter

Keyword(s):

Fault Diagnosis ◽

Multiple Model ◽

Industrial Systems ◽

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Fault diagnosis of gas turbine engines: A symbolic multiple model approach

2014 European Control Conference (ECC) ◽

10.1109/ecc.2014.6862358 ◽

2014 ◽

Author(s):

Zahra Abbasfard ◽

Amir Baniamerian ◽

Khashayar Khorasani

Keyword(s):

Fault Diagnosis ◽

Gas Turbine ◽

Turbine Engines ◽

Gas Turbine Engines ◽

Multiple Model ◽

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A strong tracking filter based multiple model approach for gas turbine fault diagnosis

Journal of Mechanical Science and Technology ◽

10.1007/s12206-017-1248-0 ◽

2018 ◽

Vol 32 (1) ◽

pp. 465-479 ◽

Author(s):

Qingcai Yang ◽

Shuying Li ◽

Yunpeng Cao

Keyword(s):

Fault Diagnosis ◽

Gas Turbine ◽

Multiple Model ◽

Strong Tracking Filter ◽

Tracking Filter ◽

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Ontology and SDG-based framework for fault diagnosis in chemical process

Future Communication Technology ◽

10.2495/icct130271 ◽

2014 ◽

Author(s):

Dong Gao ◽

Xin Xu ◽

Xin Ma

Keyword(s):

Fault Diagnosis ◽

Chemical Process

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Application of nonlinear feature extraction and least square support vector machines for fault diagnosis of chemical process

Journal of Computer Applications ◽

10.3724/sp.j.1087.2010.00236 ◽

2010 ◽

Vol 30 (1) ◽

pp. 236-239 ◽

Author(s):

Liang XU

Keyword(s):

Feature Extraction ◽

Fault Diagnosis ◽

Support Vector Machines ◽

Chemical Process ◽

Least Square ◽

Support Vector ◽

Vector Machines ◽

Nonlinear Feature Extraction ◽

Nonlinear Feature

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Multiple-Model Approach to Over-the-Horizon Radar Tracking Based on Target Perceivability

IEEE Transactions on Aerospace and Electronic Systems ◽

10.1109/taes.2021.3098113 ◽

2021 ◽

pp. 1-1

Author(s):

Leilei Guo ◽

Jian Lan ◽

X. Rong Li

Keyword(s):

Multiple Model ◽

Radar Tracking ◽

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Intelligent Fault Diagnosis of Chemical Process Based on twin convolution Deep Neural Network of Wavelet Transform

2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS) ◽

10.1109/ddcls52934.2021.9455551 ◽

2021 ◽

Author(s):

Ting Yu ◽

Yongsheng Qi

Keyword(s):

Neural Network ◽

Wavelet Transform ◽

Fault Diagnosis ◽

Chemical Process ◽

Deep Neural Network ◽

Intelligent Fault Diagnosis

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A novel one‐dimensional convolutional neural network architecture for chemical process fault diagnosis

The Canadian Journal of Chemical Engineering ◽

10.1002/cjce.24087 ◽

2021 ◽

Author(s):

Xin Niu ◽

Xia Yang

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Convolutional Neural Network ◽

Network Architecture ◽

Chemical Process ◽

Neural Network Architecture ◽

One Dimensional ◽

Process Fault Diagnosis

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