Fault detection for gas turbines based on long-term prediction using self-organizing fuzzy neural networks

Yong Jie Zhai*, Xue Wu Dai, Qian Zhou

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)

Abstract

For real-time condition monitoring and fault detection of dual-lane controlled systems, reduced order models and long-term prediction are required. In this paper fault detection of reduced order model of nonlinear systems based on long-term prediction is proposed by using self-organizing fuzzy neural network (SOFNN). The main advantages of SOFNN are that, firstly, it is very user friendly as it can automatically determine the model structure and identify the model parameters without requiring the in-depth knowledge about fuzzy systems and neural networks; secondly, it provides the excellent modeling accuracy. Data gathered at an aero engine test-bed serve as the test vehicle to demonstrate the long-term prediction. A fault detection system is designed by using SOFNN. SOFNN is trained and used to simulate system dynamic characteristic. The simulation result is compared with actual output, and then fault error is drawn. The simulation result shows that, SOFNN can simulate the system more accurately, thus the change of residual error is easy to be detected. This assures the validity of this fault detection system.

Original languageEnglish
Title of host publicationProceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1120-1125
Number of pages6
ISBN (Print)9781424409730
DOIs
Publication statusPublished - 19 Aug 2007
Externally publishedYes
Event6th International Conference on Machine Learning and Cybernetics, ICMLC 2007 - Hong Kong, China
Duration: 19 Aug 200722 Aug 2007

Publication series

NameProceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007
Volume2

Conference

Conference6th International Conference on Machine Learning and Cybernetics, ICMLC 2007
Country/TerritoryChina
CityHong Kong
Period19/08/0722/08/07

Fingerprint

Dive into the research topics of 'Fault detection for gas turbines based on long-term prediction using self-organizing fuzzy neural networks'. Together they form a unique fingerprint.

Cite this