A genetic algorithm approach to measurement prescription in fault diagnosis

1999 ◽  
Vol 120 (1-4) ◽  
pp. 223-237 ◽  
Author(s):  
Benjamin Han ◽  
Shie-Jue Lee
Keyword(s):  
Genetic Algorithm ◽  
Fault Diagnosis ◽  
Genetic Algorithm Approach

Fault Diagnosis of a Two Spool Turbo-Fan Engine Using Transient Data: A Genetic Algorithm Approach

2003 ◽  
Author(s):  
Suresh Sampath ◽  
Y. G. Li ◽  
S. O. T. Ogaji ◽  
Riti Singh
Keyword(s):  
Genetic Algorithm ◽  
Fault Diagnosis ◽  
Simulated Data ◽  
Operating Conditions ◽  
Turbofan Engine ◽  
Transient Model ◽  
Turbine Performance ◽  
Genetic Algorithm Approach ◽  
Transient Data ◽  
Cumulative Deviation

Traditionally engine fault diagnosis has been performed at steady state conditions. There are several problems which can only be detected by transient data analysis like bearing fault, some control problems etc.. In addition, gas turbine performance deviation due to a component fault is more likely to be magnified during transients, when compared with the same parameter deviations at steady states. The specific approach used in this paper is to compare model-based information with measured data obtained from the engine during a slam acceleration. The measured transient data (from actual engine) is compared with a set of simulated data from the engine transient model, under similar operating conditions and known faults through a Cumulative Deviation. The Cumulative Deviations obtained from the comparisons are minimized for the best match using Genetic Algorithm. The Genetic Algorithm has been tailored to use real coding [1] method and to meet the requirements of the new procedure. The paper describes the application of the approach to a 2-spool turbofan engine and discusses the preliminary studies conducted.

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