Applications of object-oriented approaches to neural networks in fault diagnosis

Shao Hung Chang; Jiann Liang Chen; Huan Wen Tzeng; Chin Ming Hong

Applications of object-oriented approaches to neural networks in fault diagnosis

Shao Hung Chang^*, Jiann Liang Chen, Huan Wen Tzeng, Chin Ming Hong

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

A fault diagnosis system incorporating object-oriented programming models into a neural network is developed and reported in the paper. At the same time, to draw an inference efficiently, back-propagation learning rules, statistical process control, and alpha-beta depth-first algorithm are also embedded in the system. For the purpose of fault diagnosis, the object-oriented multilayer perceptron network is first trained by the back-propagation learning rule. Then, the statistical process control is used to analyze the trends by historical data and detect suspicious components. At last, by means of the alpha-beta search technology, the most plausible fault candidates and the rank of those candidates are generated speedily.

Original language	English
Title of host publication	Proceedings of the IEEE Conference on Decision and Control
Publisher	Publ by IEEE
Pages	3708-3709
Number of pages	2
ISBN (Print)	0780312988
Publication status	Published - 1993
Externally published	Yes
Event	Proceedings of the 32nd IEEE Conference on Decision and Control. Part 2 (of 4) - San Antonio, TX, USA Duration: 1993 Dec 15 → 1993 Dec 17

Publication series

Name	Proceedings of the IEEE Conference on Decision and Control
Volume	4
ISSN (Print)	0191-2216

Conference

Conference	Proceedings of the 32nd IEEE Conference on Decision and Control. Part 2 (of 4)
City	San Antonio, TX, USA
Period	1993/12/15 → 1993/12/17

ASJC Scopus subject areas

Control and Systems Engineering
Modelling and Simulation
Control and Optimization

Cite this

Applications of object-oriented approaches to neural networks in fault diagnosis. / Chang, Shao Hung; Chen, Jiann Liang; Tzeng, Huan Wen et al.
Proceedings of the IEEE Conference on Decision and Control. Publ by IEEE, 1993. p. 3708-3709 (Proceedings of the IEEE Conference on Decision and Control; Vol. 4).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Chang, SH, Chen, JL, Tzeng, HW & Hong, CM 1993, Applications of object-oriented approaches to neural networks in fault diagnosis. in Proceedings of the IEEE Conference on Decision and Control. Proceedings of the IEEE Conference on Decision and Control, vol. 4, Publ by IEEE, pp. 3708-3709, Proceedings of the 32nd IEEE Conference on Decision and Control. Part 2 (of 4), San Antonio, TX, USA, 1993/12/15.

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abstract = "A fault diagnosis system incorporating object-oriented programming models into a neural network is developed and reported in the paper. At the same time, to draw an inference efficiently, back-propagation learning rules, statistical process control, and alpha-beta depth-first algorithm are also embedded in the system. For the purpose of fault diagnosis, the object-oriented multilayer perceptron network is first trained by the back-propagation learning rule. Then, the statistical process control is used to analyze the trends by historical data and detect suspicious components. At last, by means of the alpha-beta search technology, the most plausible fault candidates and the rank of those candidates are generated speedily.",

author = "Chang, {Shao Hung} and Chen, {Jiann Liang} and Tzeng, {Huan Wen} and Hong, {Chin Ming}",

year = "1993",

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AB - A fault diagnosis system incorporating object-oriented programming models into a neural network is developed and reported in the paper. At the same time, to draw an inference efficiently, back-propagation learning rules, statistical process control, and alpha-beta depth-first algorithm are also embedded in the system. For the purpose of fault diagnosis, the object-oriented multilayer perceptron network is first trained by the back-propagation learning rule. Then, the statistical process control is used to analyze the trends by historical data and detect suspicious components. At last, by means of the alpha-beta search technology, the most plausible fault candidates and the rank of those candidates are generated speedily.

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ER -

Applications of object-oriented approaches to neural networks in fault diagnosis

Abstract

Publication series

Conference

ASJC Scopus subject areas

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