A knowledge base system for rotary equipment fault detection and diagnosis

2010 11th International Conference on Control Automation Robotics & Vision ◽

10.1109/icarcv.2010.5707843 ◽

2010 ◽

Cited By ~ 1

Author(s):

J.-H. Zhou ◽

Louis Wee ◽

Z.-W. Zhong

Keyword(s):

Fault Detection ◽

Knowledge Base ◽

Fault Detection And Diagnosis ◽

Base System ◽

Knowledge Base System ◽

Detection And Diagnosis ◽

Rotary Equipment

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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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A phonological knowledge base system using unification-based formalism

Proceedings of the 13th conference on Computational linguistics - ◽

10.3115/991146.991160 ◽

1990 ◽

Author(s):

Hee-Sung Chung

Keyword(s):

Knowledge Base ◽

Base System ◽

Phonological Knowledge ◽

Knowledge Base System

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Design and implementation of ontology-based knowledge base system for marine hydrocarbon geology

Journal of Computer Applications ◽

10.3724/sp.j.1087.2010.00532 ◽

2010 ◽

Vol 30 (2) ◽

pp. 532-536

Author(s):

Guo-he LI ◽

Xin-ying YANG ◽

Ting YE ◽

Hong-jun SUN ◽

Xian-ming TANG ◽

...

Keyword(s):

Knowledge Base ◽

Base System ◽

Design And Implementation ◽

Knowledge Base System

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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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A Hybrid Method for Process Fault Detection and Diagnosis

IFAC Proceedings Volumes ◽

10.3182/20131218-3-in-2045.00099 ◽

2013 ◽

Vol 46 (32) ◽

pp. 827-832 ◽

Cited By ~ 7

Author(s):

Md Raihan Mallick ◽

Syed A Imtiaz

Keyword(s):

Fault Detection ◽

Hybrid Method ◽

Fault Detection And Diagnosis ◽

Detection And Diagnosis

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Fault detection and diagnosis for multiple faults of VAV terminals using self-adaptive model and layered random forest

Building and Environment ◽

10.1016/j.buildenv.2021.107667 ◽

2021 ◽

Vol 193 ◽

pp. 107667

Author(s):

Haitao Wang ◽

Daoguang Feng ◽

Kai Liu

Keyword(s):

Random Forest ◽

Fault Detection ◽

Fault Detection And Diagnosis ◽

Adaptive Model ◽

Multiple Faults ◽

Detection And Diagnosis ◽

Self Adaptive

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