A Modified Discrete Binary Ant Colony Optimization and Its Application in Chemical Process Fault Diagnosis

Lecture Notes in Computer Science - Simulated Evolution and Learning ◽

10.1007/11903697_112 ◽

2006 ◽

pp. 889-896 ◽

Cited By ~ 4

Author(s):

Ling Wang ◽

Jinshou Yu

Keyword(s):

Fault Diagnosis ◽

Ant Colony Optimization ◽

Chemical Process ◽

Ant Colony ◽

Process 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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Development and classification of expert systems for chemical process fault diagnosis

Robotics and Computer-Integrated Manufacturing ◽

10.1016/0736-5845(88)90015-4 ◽

1988 ◽

Vol 4 (3-4) ◽

pp. 437-446 ◽

Cited By ~ 3

Author(s):

Mark A. Kramer ◽

F. Eric Finch

Keyword(s):

Fault Diagnosis ◽

Expert Systems ◽

Chemical Process ◽

Process Fault Diagnosis

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Fault Diagnosis Model Based on the Fusion of Hybrid Neural Network And Ant Colony Optimization Algorithm

Third International Conference on Natural Computation (ICNC 2007) ◽

10.1109/icnc.2007.378 ◽

2007 ◽

Cited By ~ 2

Author(s):

Zhisheng Zhang ◽

Yaming Sun

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Ant Colony Optimization ◽

Optimization Algorithm ◽

Ant Colony ◽

Ant Colony Optimization Algorithm ◽

Hybrid Neural Network ◽

Model Based ◽

Diagnosis Model

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Chemical process fault diagnosis based on mixup-convolution neural network

2019 14th IEEE International Conference on Electronic Measurement & Instruments (ICEMI) ◽

10.1109/icemi46757.2019.9101484 ◽

2019 ◽

Author(s):

Gu Xiaohua ◽

Song Hongfei ◽

Wang Tian ◽

Lu Fei ◽

Li Renjie

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Chemical Process ◽

Convolution Neural Network ◽

Process Fault Diagnosis

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Entropy principal component analysis and its application to nonlinear chemical process fault diagnosis

Asia-Pacific Journal of Chemical Engineering ◽

10.1002/apj.1813 ◽

2014 ◽

Vol 9 (5) ◽

pp. 696-706 ◽

Cited By ~ 6

Author(s):

Xiaogang Deng ◽

Xuemin Tian

Keyword(s):

Principal Component Analysis ◽

Fault Diagnosis ◽

Chemical Process ◽

Principal Component ◽

Component Analysis ◽

Process Fault Diagnosis

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Chemical process fault diagnosis using kernel retrofitted fuzzy genetic algorithm based learner (FGAL) with a hidden Markov model

Lecture Notes in Computer Science - Methodology and Tools in Knowledge-Based Systems ◽

10.1007/3-540-64582-9_748 ◽

1998 ◽

pp. 190-199

Author(s):

I. Burak Özyurt ◽

Aydin K. Sunol ◽

Lawrence O. Hall

Keyword(s):

Genetic Algorithm ◽

Fault Diagnosis ◽

Markov Model ◽

Hidden Markov Model ◽

Chemical Process ◽

Hidden Markov ◽

Fuzzy Genetic Algorithm ◽

Process Fault Diagnosis ◽

Fuzzy Genetic

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Fault diagnosis of PTA oxidation process based on decision tree and ant colony optimization

2008 Chinese Control and Decision Conference ◽

10.1109/ccdc.2008.4597361 ◽

2008 ◽

Author(s):

Xiaoxia Zheng ◽

Feng Qian

Keyword(s):

Fault Diagnosis ◽

Decision Tree ◽

Ant Colony Optimization ◽

Oxidation Process ◽

Ant Colony

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Bidirectional Recurrent Neural Network-Based Chemical Process Fault Diagnosis

Industrial & Engineering Chemistry Research ◽

10.1021/acs.iecr.9b05885 ◽

2019 ◽

Vol 59 (2) ◽

pp. 824-834 ◽

Cited By ~ 4

Author(s):

Shuyuan Zhang ◽

Kexin Bi ◽

Tong Qiu

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Recurrent Neural Network ◽

Chemical Process ◽

Process Fault Diagnosis

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Chemical process fault diagnosis based on enchanted machine‐learning approach

The Canadian Journal of Chemical Engineering ◽

10.1002/cjce.23642 ◽

2019 ◽

Vol 97 (12) ◽

pp. 3074-3086 ◽

Cited By ~ 1

Author(s):

Xiaohui Yang ◽

Jingsong Zhou ◽

Zihao Xie ◽

Gong Ke

Keyword(s):

Machine Learning ◽

Fault Diagnosis ◽

Chemical Process ◽

Learning Approach ◽

Machine Learning Approach ◽

Process Fault Diagnosis

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Feature selection for chemical process fault diagnosis by artificial immune systems

Chinese Journal of Chemical Engineering ◽

10.1016/j.cjche.2017.09.023 ◽

2018 ◽

Vol 26 (8) ◽

pp. 1599-1604 ◽

Cited By ~ 3

Author(s):

Liang Ming ◽

Jinsong Zhao

Keyword(s):

Feature Selection ◽

Fault Diagnosis ◽

Chemical Process ◽

Artificial Immune Systems ◽

Artificial Immune ◽

Immune Systems ◽

Selection For ◽

Process Fault Diagnosis

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