Evolution strategy for gas-turbine fault-diagnoses. Ogaji, S., Sampath, S., Marinai, L., Singh, R., & Probert, S. Applied Energy, 81(2):222-230, 6, 2005.
Evolution strategy for gas-turbine fault-diagnoses [link]Website  abstract   bibtex   
The aim of this investigation is to be able to diagnose gas-path faults in gas turbines by minimising the differences between the observed and simulated data for the engine’s behaviour. The simulated data are generated using a known set of faults as the input to the engine-behaviour aero-thermo model and an appropriate objective function is minimised to yield the best solution to the problem. The application of evolution strategy (ES) in the search for this minimum is an effective, flexible, robust and reliable way of solving engine-diagnostics problems. Adopting this approach leads to a considerable reduction in the overall time taken to obtain a convergent solution when compared with that required using a simple genetic-based algorithm.
@article{
 title = {Evolution strategy for gas-turbine fault-diagnoses},
 type = {article},
 year = {2005},
 identifiers = {[object Object]},
 keywords = {ANN,Artificial neural-network,BBN,Bayesian belief network,Diagnostics,ES,Evolution strategy,GA,GPA,Gas-path analysis,Genetic algorithm,HP,High pressure,MOPA,MSA,Multiple operating-point analysis,Mutative step-size adaptation,NG,NM,NS,Number of generations,Number of measurements,Number of strings,PC,PDF,PM,PS,Performance,Population size,Probability density function,Probability of crossover,Probability of mutation,SOPA,Single operating-point analysis,TPM,TQM,Total productive management,Total quality management},
 pages = {222-230},
 volume = {81},
 websites = {http://www.sciencedirect.com/science/article/pii/S0306261904001023},
 month = {6},
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 abstract = {The aim of this investigation is to be able to diagnose gas-path faults in gas turbines by minimising the differences between the observed and simulated data for the engine’s behaviour. The simulated data are generated using a known set of faults as the input to the engine-behaviour aero-thermo model and an appropriate objective function is minimised to yield the best solution to the problem. The application of evolution strategy (ES) in the search for this minimum is an effective, flexible, robust and reliable way of solving engine-diagnostics problems. Adopting this approach leads to a considerable reduction in the overall time taken to obtain a convergent solution when compared with that required using a simple genetic-based algorithm.},
 bibtype = {article},
 author = {Ogaji, S.O.T. and Sampath, S. and Marinai, L. and Singh, R. and Probert, S.D.},
 journal = {Applied Energy},
 number = {2}
}
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