Fuzzy diagnosis of turbomachines

Authors:
Meijun Yang;Qiang Shen
Affiliations:
(Correspd. E-mail: mmy@aber.ac.uk) Department of Electronics and Informatics, Guangdong, Baiyun Institute, China;Department of Computer Sciences, Aberystwyth University, Wales, UK
Venue:
International Journal of Knowledge-based and Intelligent Engineering Systems
Year:
2008

Citing 6
Cited 1

The fuzzy systems handbook: a practitioner's guide to building, using, and maintaining fuzzy systems

The fuzzy systems handbook: a practitioner's guide to building, using, and maintaining fuzzy systems
Rough Sets: Theoretical Aspects of Reasoning about Data

Rough Sets: Theoretical Aspects of Reasoning about Data
Machine Learning

Machine Learning
Gas-Turbine Condition Monitoring Using Qualitative Model-Based Diagnosis

IEEE Expert: Intelligent Systems and Their Applications
VIBEX: an expert system for vibration fault diagnosis of rotating machinery using decision tree and decision table

Expert Systems with Applications: An International Journal
From approximative to descriptive fuzzy classifiers

IEEE Transactions on Fuzzy Systems

Reinforcing fuzzy rule-based diagnosis of turbomachines with case-based reasoning

International Journal of Knowledge-based and Intelligent Engineering Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a fuzzy knowledge-based system for turbomachinery diagnosis. Given symptoms associated with a vibration problem, the system can identify and rank possible causes by performing incremental forward chaining. The diagnostic system incorporates an attribute weighting component to reflect the relative significance of conditional attributes, thereby allowing the system to produce more accurate diagnoses. The ability of this system to identify causes of typical vibration problems in rotating machinery is supported with tests on real cases.