Real-time diagnosis of hydrogen gas system and seal-oil system of a large thermal turbo-generator using a fuzzy-set expert system

Li Wang, Chien I. Chen

Research output: Contribution to journalArticle

Abstract

A diagnostic expert system with real-time knowledge-based functions for monitoring the performance of both hydrogen gas system and seal-oil system of a large thermal power turbo-generator is presented in this paper. The proposed diagnostic system evaluates the operating conditions of the studied systems in real time using several inputs from a number of sensors such as oil pressure, oil level, hydrogen purity, electrical signals, etc. When any abnormal situation occurs, the proposed diagnostic system may quickly and extensively analyze the operating conditions and discover the abnormalities. The proposed system can also provide intelligent suggestions to infer the specific equipment status and recommendations for remedial action. The operating data of a practical thermal power plant, the Shen-Ao 3rd generator set of Taiwan Power Company (TPC), Taiwan, are employed to justify the performance of the proposed diagnostic expert system. It can be concluded from the simulation results that the proposed expert system has the ability to make correct diagnosis in real time on the studied hydrogen gas system and seal-oil system under abnormal operating conditions.

Original languageEnglish
Pages (from-to)269-277
Number of pages9
JournalInternational Journal of Electrical Engineering
Volume14
Issue number4
Publication statusPublished - 2007 Aug 1

Fingerprint

Fuzzy sets
Expert systems
Seals
Hydrogen
Gases
Power plants
Oils
Hot Temperature
Monitoring
Sensors
Industry

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

Cite this

@article{eda472c746204df9a7a808d3de90c96e,
title = "Real-time diagnosis of hydrogen gas system and seal-oil system of a large thermal turbo-generator using a fuzzy-set expert system",
abstract = "A diagnostic expert system with real-time knowledge-based functions for monitoring the performance of both hydrogen gas system and seal-oil system of a large thermal power turbo-generator is presented in this paper. The proposed diagnostic system evaluates the operating conditions of the studied systems in real time using several inputs from a number of sensors such as oil pressure, oil level, hydrogen purity, electrical signals, etc. When any abnormal situation occurs, the proposed diagnostic system may quickly and extensively analyze the operating conditions and discover the abnormalities. The proposed system can also provide intelligent suggestions to infer the specific equipment status and recommendations for remedial action. The operating data of a practical thermal power plant, the Shen-Ao 3rd generator set of Taiwan Power Company (TPC), Taiwan, are employed to justify the performance of the proposed diagnostic expert system. It can be concluded from the simulation results that the proposed expert system has the ability to make correct diagnosis in real time on the studied hydrogen gas system and seal-oil system under abnormal operating conditions.",
author = "Li Wang and Chen, {Chien I.}",
year = "2007",
month = "8",
day = "1",
language = "English",
volume = "14",
pages = "269--277",
journal = "International Journal of Electrical Engineering",
issn = "1812-3031",
publisher = "Chinese Institute of Electrical Engineering",
number = "4",

}

TY - JOUR

T1 - Real-time diagnosis of hydrogen gas system and seal-oil system of a large thermal turbo-generator using a fuzzy-set expert system

AU - Wang, Li

AU - Chen, Chien I.

PY - 2007/8/1

Y1 - 2007/8/1

N2 - A diagnostic expert system with real-time knowledge-based functions for monitoring the performance of both hydrogen gas system and seal-oil system of a large thermal power turbo-generator is presented in this paper. The proposed diagnostic system evaluates the operating conditions of the studied systems in real time using several inputs from a number of sensors such as oil pressure, oil level, hydrogen purity, electrical signals, etc. When any abnormal situation occurs, the proposed diagnostic system may quickly and extensively analyze the operating conditions and discover the abnormalities. The proposed system can also provide intelligent suggestions to infer the specific equipment status and recommendations for remedial action. The operating data of a practical thermal power plant, the Shen-Ao 3rd generator set of Taiwan Power Company (TPC), Taiwan, are employed to justify the performance of the proposed diagnostic expert system. It can be concluded from the simulation results that the proposed expert system has the ability to make correct diagnosis in real time on the studied hydrogen gas system and seal-oil system under abnormal operating conditions.

AB - A diagnostic expert system with real-time knowledge-based functions for monitoring the performance of both hydrogen gas system and seal-oil system of a large thermal power turbo-generator is presented in this paper. The proposed diagnostic system evaluates the operating conditions of the studied systems in real time using several inputs from a number of sensors such as oil pressure, oil level, hydrogen purity, electrical signals, etc. When any abnormal situation occurs, the proposed diagnostic system may quickly and extensively analyze the operating conditions and discover the abnormalities. The proposed system can also provide intelligent suggestions to infer the specific equipment status and recommendations for remedial action. The operating data of a practical thermal power plant, the Shen-Ao 3rd generator set of Taiwan Power Company (TPC), Taiwan, are employed to justify the performance of the proposed diagnostic expert system. It can be concluded from the simulation results that the proposed expert system has the ability to make correct diagnosis in real time on the studied hydrogen gas system and seal-oil system under abnormal operating conditions.

UR - http://www.scopus.com/inward/record.url?scp=37148999738&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=37148999738&partnerID=8YFLogxK

M3 - Article

VL - 14

SP - 269

EP - 277

JO - International Journal of Electrical Engineering

JF - International Journal of Electrical Engineering

SN - 1812-3031

IS - 4

ER -