Incorporating Neural Networks Into Gas Turbine Performance Diagnostics

Abstract

Possibilities of incorporating neural nets in different tasks of a gas turbine performance diagnostic procedure are investigated. The purpose is to examine how neural nets can be implemented and what advantages they may offer. First, the possibility to constitute a performance model by using neural nets is considered. Different modes of operation are examined and the neural net architectures for achieving better accuracy are discussed. Subsequently, different problems of fault detection and identification are considered. Classification of faults is performed on the basis of diagnostic parameters produced by adaptive modelling. Both sensor faults and actual engine component faults are examined. A decision logic based on several neural nets is proposed. At a first level it is decided whether a fault exists, and if yes, checks are performed in order to identify the fault in as much detail as possible. Summarizing, the paper discusses different aspects of neural net implementation, in an effort to provide guidelines for application of this type of technique in the field of gas turbine diagnostics.