Fault detection and diagnosis in the chemical process

Lecture Notes in Control and Information Sciences - Auxiliary Signal Design in Fault Detection and Diagnosis ◽

10.1007/bfb0009319 ◽

2005 ◽

pp. 157-187

Keyword(s):

Fault Detection ◽

Chemical Process ◽

Fault Detection And Diagnosis ◽

Detection And Diagnosis

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Distributed Fault Detection and Diagnosis of Chemical Process Based on MAS

The Fourth International Conference on Control and Automation 2003 ICCA Final Program and Book of Abstracts ICCA-03 ◽

10.1109/icca.2003.1595091 ◽

2003 ◽

Author(s):

Jinggao Sun ◽

Xinbin Yang ◽

Dao Huang

Keyword(s):

Fault Detection ◽

Chemical Process ◽

Fault Detection And Diagnosis ◽

Distributed Fault Detection ◽

Detection And Diagnosis

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Distributed fault detection and diagnosis of chemical process based on MAS

The Fourth International Conference on Control and Automation 2003 ICCA Final Program and Book of Abstracts ICCA-03 ◽

10.1109/icca.2003.1229173 ◽

2005 ◽

Author(s):

Jinggao Sun ◽

Xinbin Yang ◽

Dao Huang

Keyword(s):

Fault Detection ◽

Chemical Process ◽

Fault Detection And Diagnosis ◽

Distributed Fault Detection ◽

Detection And Diagnosis

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Fault detection and diagnosis of chemical process using enhanced KECA

Chemometrics and Intelligent Laboratory Systems ◽

10.1016/j.chemolab.2016.12.013 ◽

2017 ◽

Vol 161 ◽

pp. 61-69 ◽

Cited By ~ 21

Author(s):

Haili Zhang ◽

Yongsheng Qi ◽

Lin Wang ◽

Xuejin Gao ◽

Xichang Wang

Keyword(s):

Fault Detection ◽

Chemical Process ◽

Fault Detection And Diagnosis ◽

Detection And Diagnosis

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Fault Detection and Diagnosis in a Chemical Process using Long Short-Term Memory Recurrent Neural Network

2018 International Joint Conference on Neural Networks (IJCNN) ◽

10.1109/ijcnn.2018.8489385 ◽

2018 ◽

Author(s):

Gilberto M. Xavier ◽

Jose Manoel de Seixas

Keyword(s):

Neural Network ◽

Fault Detection ◽

Recurrent Neural Network ◽

Chemical Process ◽

Short Term Memory ◽

Fault Detection And Diagnosis ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory ◽

Detection And Diagnosis

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Review on chemical process fault detection and diagnosis

2017 6th International Symposium on Advanced Control of Industrial Processes (AdCONIP) ◽

10.1109/adconip.2017.7983824 ◽

2017 ◽

Cited By ~ 6

Author(s):

Liang Ming ◽

Jinsong Zhao

Keyword(s):

Fault Detection ◽

Chemical Process ◽

Fault Detection And Diagnosis ◽

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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