Fuzzy neural network modeling for fault diagnosis in turbine startup of a power plant

被引：0

作者：

Cheng Weiliang ^{[1
]}

Xia Guodong ^{[1
]}

Sun Hongyu ^{[1
]}

机构：

[1] N China Elect Power Univ, Dept Power Engn, Beijing 102206, Peoples R China

来源：

Proceedings of the ASME Power Conference 2005, Pts A and B | 2005年

关键词：

D O I：

暂无

中图分类号：

O414.1 [热力学];

学科分类号：

摘要：

A fault set and a symptom set were established in order to exactly judge and to quickly dispose in turbine startup of a power plant. There are ten typical faults in the fault set and sixteen fault symptoms in the symptom set. In consideration of the various kinds of change directions and ranges of the fault symptom parameters, the fuzzy disposal of nine degrees is put forward to build a set of typical fault-character-sample mode. A neural network model for fault diagnosis was obtained by fuzzy theory and radial basis function, and it was validated by using evaluator. It shows that the fuzzy fault disposal and the swiftness of training constringency are very satisfied in turbine startup of this power plant.

引用

页码：383 / 386

页数：4

共 50 条

[1] Application of fuzzy neural network to the nuclear power plant in process fault diagnosis
Liu Yong-Kuo
Xia Hong
Xie Chun-li
[J]. Journal of Marine Science and Application, 2005, 4 (1) : 34 - 38
[2] Application of fuzzy neural network to the nuclear power plant in process fault diagnosis
LIU Yong-kuo 1
[J]. Journal of Marine Science and Application, 2005, (01) : 34 - 38
[3] Application of fuzzy neural network to the nuclear power plant in process fault diagnosis
Liu Yong-kuo
Xia Hong
Xie Chun-li
[J]. JOURNAL OF MARINE SCIENCE AND APPLICATION, 2005, 4 (01) : 34 - 38
[4] Fuzzy and Neural Network Approaches to Wind Turbine Fault Diagnosis
Farsoni, Saverio
Simani, Silvio
Castaldi, Paolo
[J]. APPLIED SCIENCES-BASEL, 2021, 11 (11):
[5] Application of Fuzzy Neural Network to Fault Diagnosis of Sensor with Mechanical Properties in Nuclear Power Plant
Li, Jinyang
Xia, Hong
[J]. ADVANCED RESEARCH ON INTELLIGENT SYSTEMS AND MECHANICAL ENGINEERING, 2013, 644 : 68 - 71
[6] Fault Diagnosis in Power Plant Based on Multi-Neural Network
Xia Fei
Zhang Hao
Peng Daogang
[J]. 2014 IEEE INTERNATIONAL CONFERENCE ON SYSTEM SCIENCE AND ENGINEERING (ICSSE), 2014, : 180 - 184
[7] Startup optimization of a combined cycle power plant based on cooperative fuzzy reasoning and a neural network
Matsumoto, H
Ohsawa, Y
Takahasi, S
Akiyama, T
Hanaoka, H
Ishiguro, O
[J]. IEEE TRANSACTIONS ON ENERGY CONVERSION, 1997, 12 (01) : 51 - 59
[8] Neural Network Modeling of Aircraft Power Plant and Fault Diagnosis Method Using Time Frequency Analysis
Wei, Liao
Hua, Wang
Pu, Han
[J]. CCDC 2009: 21ST CHINESE CONTROL AND DECISION CONFERENCE, VOLS 1-6, PROCEEDINGS, 2009, : 353 - +
[9] Startup fault detection and diagnosis in turbine engines
Menon, S
Uluyol, N
Kim, K
Nwadiogbu, EO
[J]. 2003 IEEE AEROSPACE CONFERENCE PROCEEDINGS, VOLS 1-8, 2003, : 3069 - 3074
[10] Fuzzy Neural Network Approach for Fault Diagnosis of Power Plant Thermal System under Different Operating Points
Ma, Liangyu
Lee, Kwang Y.
[J]. 2008 IEEE POWER & ENERGY SOCIETY GENERAL MEETING, VOLS 1-11, 2008, : 1054 - 1060

← 1 2 3 4 5 →