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《Acta Aeronautica Et Astronautica Sinica》 2005-06
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Some Studies in Aero-engine Fault Diagnosis Using Support Vector Machines

XU Qi-hua~1,SHI Jun~2(1.Electronic Engineering Department,Huaihai Institute of Technology,Lianyungang 222005,China)(2.College of Automatic Control,Northwestern Polytechnical University,Xi′an 710072,China)  
Support vector machines can avoid over-fitting and have better generalization ability as compared with neural networks.In this paper,the support vector machines based fault diagnosis algorithms are developed for aero-engines.The influence of kernel function is discussed and the chunking algorithms are analyzed.The methods are presented to select normalized parameter and kernel parameter.With the proposed algorithms,the support vector machines can give correct fault diagnosis results for the gas path components of an aero-engine.The results show that the fault diagnosis algorithms are also able to meet the application requirements and can keep robust when the measurement inputs are disturbed by noises.
【Fund】: 江苏省高校自然科学研究计划(04KJD510018);; 连云港市科技计划(GY200401)资助项目
【CateGory Index】: V263.6;
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