A Fuzzy Rule Based Dystem for Fault Diagnosis, Using Oil Analysis Results

Maintenance, as a support function, plays an important roe in manufacturing companies and operational organizations. In this paper, fuzzy rules used to interpret linguistic variables for determination of priorities. Using this approach, such verbal expressions, which cannot be explicitly analyzed or statistically expressed, ard herein quantified and used in decision making. In this research, it is intended to justify the importance of historix data in oil analysis for fault detection, Initial rules derived by desision trees and visualization then these fault diagnosis rules corrected by experts. with the access to decent information sources, the wear behaviours of diesel engines are studied . Also , the relation between the final status of engine and selected features in oil analysis is analyzed . The dissertation and analysis of determining effective features in condition monitoring of equipments and their contribution, is the issue that has been studied through a Data Mining model.

Keywords:

Condition Monitoring , Oil Analysis , Wear Behavior , Fuzzy Rule Based System