Optimal input design for fault detection and diagnosis

Proceedings of 1995 American Control Conference - ACC'95 ◽

10.1109/acc.1995.520927 ◽

2005 ◽

Author(s):

P. Sadegh ◽

H. Madsen ◽

J. Holst

Keyword(s):

Fault Detection ◽

Fault Detection And Diagnosis ◽

Optimal Input ◽

Input Design ◽

Optimal Input Design ◽

Detection And Diagnosis

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Bayesian Approach to Optimal Input Design for Failure Detection and Diagnosis

IFAC Proceedings Volumes ◽

10.1016/s1474-6670(17)52773-1 ◽

1990 ◽

Vol 23 (1) ◽

pp. 525-529

Author(s):

F. Kerestecioğlu ◽

M.B. Zarrop

Keyword(s):

Bayesian Approach ◽

Failure Detection ◽

Optimal Input ◽

Input Design ◽

Optimal Input Design ◽

Failure Detection And Diagnosis ◽

Detection And Diagnosis

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Optimum input design for fault detection and diagnosis: Model-based prediction and statistical distance measures

2013 European Control Conference (ECC) ◽

10.23919/ecc.2013.6669785 ◽

2013 ◽

Author(s):

Kwang-Ki K. Kim ◽

Davide M. Raimondo ◽

Richard D. Braatz

Keyword(s):

Fault Detection ◽

Fault Detection And Diagnosis ◽

Distance Measures ◽

Model Based ◽

Input Design ◽

Statistical Distance ◽

Diagnosis Model ◽

Detection And Diagnosis ◽

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BAYESIAN APPROACH TO OPTIMAL INPUT DESIGN FOR FAILURE DETECTION AND DIAGNOSIS

Adaptive Systems in Control and Signal Processing 1989 ◽

10.1016/b978-0-08-035727-0.50092-5 ◽

1990 ◽

pp. 525-529 ◽

Author(s):

F. Kerestecioğlu ◽

M.B. Zarrop

Keyword(s):

Bayesian Approach ◽

Failure Detection ◽

Optimal Input ◽

Input Design ◽

Optimal Input Design ◽

Failure Detection And Diagnosis ◽

Detection And Diagnosis

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Semidefinite programming relaxation of optimum active input design for fault detection and diagnosis: Model-based finite horizon prediction

2013 European Control Conference (ECC) ◽

10.23919/ecc.2013.6669496 ◽

2013 ◽

Author(s):

Kwang-Ki K. Kim ◽

Richard D. Braatz

Keyword(s):

Fault Detection ◽

Semidefinite Programming ◽

Fault Detection And Diagnosis ◽

Finite Horizon ◽

Semidefinite Programming Relaxation ◽

Model Based ◽

Input Design ◽

Diagnosis Model ◽

Detection And Diagnosis

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Wind turbine fault detection and diagnosis using LSTM neural network

2020 39th Chinese Control Conference (CCC) ◽

10.23919/ccc50068.2020.9188709 ◽

2020 ◽

Author(s):

Taoran Yang ◽

Jing Teng ◽

Changling Li ◽

Yizhan Feng

Keyword(s):

Neural Network ◽

Fault Detection ◽

Wind Turbine ◽

Fault Detection And Diagnosis ◽

Detection And Diagnosis

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Fault Detection and Diagnosis of Induction Machine with On-Line Parameter Programming Facility

i-manager’s Journal on Instrumentation and Control Engineering ◽

10.26634/jic.2.1.2787 ◽

2014 ◽

Vol 2 (1) ◽

pp. 1-8

Author(s):

Sheikh Rafik Manihar Ahmed ◽

Keyword(s):

Fault Detection ◽

Induction Machine ◽

Fault Detection And Diagnosis ◽

Line Parameter ◽

On Line ◽

Detection And Diagnosis

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Overview on hybrid approaches to fault detection and diagnosis: Combining data-driven, physics-based and knowledge-based models

Procedia CIRP ◽

10.1016/j.procir.2021.03.041 ◽

2021 ◽

Vol 99 ◽

pp. 278-283

Author(s):

Yannick Wilhelm ◽

Peter Reimann ◽

Wolfgang Gauchel ◽

Bernhard Mitschang

Keyword(s):

Fault Detection ◽

Fault Detection And Diagnosis ◽

Data Driven ◽

Knowledge Based ◽

Hybrid Approaches ◽

Combining Data ◽

Detection And Diagnosis

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SCADA data analytics for fault detection and diagnosis of wind turbines

2021 7th International Conference on Control, Instrumentation and Automation (ICCIA) ◽

10.1109/iccia52082.2021.9403538 ◽

2021 ◽

Author(s):

Ana Maria Peco Chacon ◽

Fausto Pedro Garcia Marquez

Keyword(s):

Fault Detection ◽

Wind Turbines ◽

Data Analytics ◽

Fault Detection And Diagnosis ◽

Detection And Diagnosis

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Fault detection and diagnosis of a blade pitch system in a floating wind turbine based on Kalman filters and artificial neural networks

Renewable Energy ◽

10.1016/j.renene.2020.12.116 ◽

2021 ◽

Author(s):

Seongpil Cho ◽

Minjoo Choi ◽

Zhen Gao ◽

Torgeir Moan

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Fault Detection ◽

Wind Turbine ◽

Kalman Filters ◽

Fault Detection And Diagnosis ◽

Floating Wind Turbine ◽

Artificial Neural ◽

Pitch System ◽

Detection And Diagnosis

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Fault detection and diagnosis for Air Handling Unit based on multiscale convolutional neural networks

Energy and Buildings ◽

10.1016/j.enbuild.2021.110795 ◽

2021 ◽

Vol 236 ◽

pp. 110795

Author(s):

Fanyong Cheng ◽

Wenjian Cai ◽

Xin Zhang ◽

Huanyue Liao ◽

Can Cui

Keyword(s):

Neural Networks ◽

Fault Detection ◽

Convolutional Neural Networks ◽

Fault Detection And Diagnosis ◽

Air Handling Unit ◽

Detection And Diagnosis

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