This paper presents the development of an integrated fault diagnostics model for identifying shifts in component performance and sensor faults using the Genetic Algorithm and Artificial Neural Network. The diagnostics model operates in two distinct stages. The first stage uses response surfaces for computing objective functions to increase the exploration potential of the search space while easing the computational burden. The second stage uses the concept of a hybrid diagnostics model in which a nested neural network is used with genetic algorithm to form a hybrid diagnostics model. The nested neural network functions as a pre-processor or filter to reduce the number of fault classes to be explored by the genetic algorithm based diagnostics model. The hybrid model improves the accuracy, reliability, and consistency of the results obtained. In addition significant improvements in the total run time have also been observed. The advanced cycle Intercooled Recuperated WR21 engine has been used as the test engine for implementing the diagnostics model.

Issue Section:
Gas Turbines: Controls, Diagnostics & Instrumentation
Keywords:
sensors, fault diagnosis, genetic algorithms, neural nets, engines, reliability, cooling
Topics:
Artificial neural networks, Engines, Genetic algorithms, Sensors, Response surface methodology
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Copyright © 2006
 by American Society of Mechanical Engineers
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