Cost-sensitive and sequential feature selection for chiller fault detection and diagnosis

International Journal of Refrigeration ◽

10.1016/j.ijrefrig.2017.11.003 ◽

2018 ◽

Vol 86 ◽

pp. 401-409 ◽

Cited By ~ 32

Author(s):

Ke Yan ◽

Lulu Ma ◽

Yuting Dai ◽

Wen Shen ◽

Zhiwei Ji ◽

...

Keyword(s):

Feature Selection ◽

Fault Detection ◽

Fault Detection And Diagnosis ◽

Sequential Feature Selection ◽

Selection For ◽

Detection And Diagnosis

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Optimal Sensor Configuration and Feature Selection for AHU Fault Detection and Diagnosis

IEEE Transactions on Industrial Informatics ◽

10.1109/tii.2016.2644669 ◽

2017 ◽

Vol 13 (3) ◽

pp. 1369-1380 ◽

Cited By ~ 12

Author(s):

Dan Li ◽

Yuxun Zhou ◽

Guoqiang Hu ◽

Costas J. Spanos

Keyword(s):

Feature Selection ◽

Fault Detection ◽

Fault Detection And Diagnosis ◽

Selection For ◽

Sensor Configuration ◽

Detection And Diagnosis

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Feature Selection for Enhancement of Bearing Fault Detection and Diagnosis Based on Self-Organizing Map

Lecture Notes in Electrical Engineering - Recent Advances in Electrical Engineering and Control Applications ◽

10.1007/978-3-319-48929-2_18 ◽

2016 ◽

pp. 233-246 ◽

Cited By ~ 6

Author(s):

Smail Haroun ◽

Amirouche Nait Seghir ◽

Said Touati

Keyword(s):

Feature Selection ◽

Fault Detection ◽

Fault Detection And Diagnosis ◽

Self Organizing Map ◽

Bearing Fault ◽

Bearing Fault Detection ◽

Selection For ◽

Detection And Diagnosis ◽

Self Organizing

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A nonlinear support vector machine‐based feature selection approach for fault detection and diagnosis: Application to the Tennessee Eastman process

AIChE Journal ◽

10.1002/aic.16497 ◽

2019 ◽

Vol 65 (3) ◽

pp. 992-1005 ◽

Cited By ~ 14

Author(s):

Melis Onel ◽

Chris A. Kieslich ◽

Efstratios N. Pistikopoulos

Keyword(s):

Support Vector Machine ◽

Feature Selection ◽

Fault Detection ◽

Fault Detection And Diagnosis ◽

Support Vector ◽

Tennessee Eastman Process ◽

Selection Approach ◽

Detection And Diagnosis ◽

Feature Selection Approach ◽

Nonlinear Support

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Unsupervised feature selection using swarm intelligence and consensus clustering for automatic fault detection and diagnosis in Heating Ventilation and Air Conditioning systems

Applied Soft Computing ◽

10.1016/j.asoc.2015.05.030 ◽

2015 ◽

Vol 34 ◽

pp. 402-425 ◽

Cited By ~ 28

Author(s):

Mitchell Yuwono ◽

Ying Guo ◽

Josh Wall ◽

Jiaming Li ◽

Sam West ◽

...

Keyword(s):

Feature Selection ◽

Fault Detection ◽

Swarm Intelligence ◽

Air Conditioning ◽

Fault Detection And Diagnosis ◽

Consensus Clustering ◽

Unsupervised Feature Selection ◽

Detection And Diagnosis ◽

Air Conditioning Systems

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Important sensors for chiller fault detection and diagnosis (FDD) from the perspective of feature selection and machine learning

International Journal of Refrigeration ◽

10.1016/j.ijrefrig.2010.08.011 ◽

2011 ◽

Vol 34 (2) ◽

pp. 586-599 ◽

Cited By ~ 52

Author(s):

H. Han ◽

B. Gu ◽

T. Wang ◽

Z.R. Li

Keyword(s):

Machine Learning ◽

Feature Selection ◽

Fault Detection ◽

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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