Study On Intelligent Fault Diagnosis System For Turbogenerator Based On RCM Analysis

Fault diagnosis is important for turbogenerator condition maintenance because fault diagnosis provides technical support for maintenance decision. How to put the result of reliability centered maintenance analysis into the process of fault diagnosis to make fault diagnosis provide more important information for condition maintenance and how to use advanced intelligent fault diagnosis technology for turbogenerator fault diagnosis to improve the accuracy of fault diagnosis result are study content in this dissertation.Reliability centered maintenance analysis method is used to analyze steam turbine vibration fault and flow path components fault in order to get some information of fault reason, fault influence, fault treatment measures and fault symptoms about the fault. The information is used to build fault diagnosis model and fault diagnosis flow. The fault diagnosis model is used to make fault diagnosis follow fault diagnosis flow. At last, fault diagnosis report is gotten to provide technical support for maintenance decision.With the purpose of reducing fault identification range, extracted principal component feature of fault is used to class fault modes into several fault classifications in order to identify the fault classification to which the fault belongs in the first step for fault diagnosis. The fault diagnosis decision table is built by using rough set theory, which is used to extract fault symptoms that are useful for identifying the fault from several fault modes included in a fault classification. So the fault diagnosis rules are optimized by this method, the other reductant rules are dismissed to descrease the influence to fault identification.The weight distribution method by expert experience is often used in the process of fault diagnosis inference to distribute the weight of the precondition of fault diagnosis rules. This method has the disadvantage of making the weight with the influence of human subjectivity. A method of weight distribution using knowledge dependency which is used to overcomes the disadvantage of weight distribution using expert experience to decrease the uncertainty in the inference process of fault diagnosis.The normal operation range of thermal parameters is obtained by using a statistic analysis method, which combined with operation rules to obtain all kinds of operation range membership degree of thermal parameters. After then, membership function of fault symptoms are got by selecting suitable function. A mapping between normal operation range of a fault systom and working condition under multi-load is built, which to solve the problem of the fault symptom membership function's automatic acquisition.With the purpose of field application, the intelligent fault diagnosis system for turbogenerator was developed to meet the requirement of condition maintenance by using latest technology of hardware and software.