Support Vector Neural Network and Principal Component Analysis for Fault Diagnosis of Analog Circuits

Industrial plants often have many process variable measurements available, which can be monitored for fault detection and diagnosis. Using all these variables as inputs to an artificial neural network for fault diagnosis can result in an impractically large network, with consequent long training times and high computational requirement during use. Principal component analysis (PCA) is investigated in this paper for generating a reduced number of variables to be used as neural network inputs for fault diagnosis. The main application described is to a real industrial nuclear fuel processing plant. A simulated chemical process was also used to assist the development of the techniques. Results in both applications demonstrate satisfactory fault diagnosis performance with a reduction in the number of neural network parameters of approximately 50 per cent using PCA. The paper also includes some introductory material on PCA and neural networks, and their application to process fault diagnosis.

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An input-training neural network based nonlinear principal component analysis approach for fault diagnosis

Proceedings of the 4th World Congress on Intelligent Control and Automation (Cat. No.02EX527) ◽

10.1109/wcica.2002.1020023 ◽

2003 ◽

Author(s):

Li Erguo ◽

Yu Jinshou

Keyword(s):

Neural Network ◽

Principal Component Analysis ◽

Fault Diagnosis ◽

Principal Component ◽

Component Analysis ◽

Analysis Approach ◽

Nonlinear Principal Component Analysis ◽

Training Neural Network

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Evaluation of principal component analysis and neural network performance for bearing fault diagnosis from vibration signal processed by RS and DF analyses

Mechanical Systems and Signal Processing ◽

10.1016/j.ymssp.2010.11.021 ◽

2011 ◽

Vol 25 (5) ◽

pp. 1765-1772 ◽

Cited By ~ 68

Author(s):

E.P. de Moura ◽

C.R. Souto ◽

A.A. Silva ◽

M.A.S. Irmão

Keyword(s):

Neural Network ◽

Principal Component Analysis ◽

Fault Diagnosis ◽

Network Performance ◽

Principal Component ◽

Vibration Signal ◽

Component Analysis ◽

Bearing Fault ◽

Bearing Fault Diagnosis

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Fault Diagnosis for PEMFC Water Management Subsystem Based on Learning Vector Quantization Neural Network and Kernel Principal Component Analysis

World Electric Vehicle Journal ◽

10.3390/wevj12040255 ◽

2021 ◽

Vol 12 (4) ◽

pp. 255

Author(s):

Shuna Jiang ◽

Qi Li ◽

Rui Gan ◽

Weirong Chen

Keyword(s):

Neural Network ◽

Water Management ◽

Principal Component Analysis ◽

Fault Diagnosis ◽

Vector Quantization ◽

High Dimension ◽

Principal Component ◽

Component Analysis ◽

Learning Vector Quantization ◽

Kernel Principal Component Analysis

To solve the problem of water management subsystem fault diagnosis in a proton exchange membrane fuel cell (PEMFC) system, a novel approach based on learning vector quantization neural network (LVQNN) and kernel principal component analysis (KPCA) is proposed. In the proposed approach, the KPCA method is used for processing strongly coupled fault data with a high dimension to reduce the data dimension and to extract new low-dimensional fault feature data. The LVQNN method is used to carry out fault recognition using the fault feature data. The effectiveness of the proposed fault detection method is validated using the experimental data of the PEMFC power system. Results show that the proposed method can quickly and accurately diagnose the three health states: normal state, water flooding failure and membrane dry failure, and the recognition accuracy can reach 96.93%. Therefore, the method proposed in this paper is suitable for processing the fault data with a high dimension and abundant quantities, and provides a reference for the application of water management subsystem fault diagnosis of PEMFC.

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