Turbofan Engine Test Vector Machine-based Fault Diagnosis

Traditional fault diagnosis using class, is under the condition of adequate samples, that is, could get sufficient recognition results only if the number of samples runs to infinite. Unfortunately, in practical issues, the number of samples is limited, and then ideal results can hardly be got on the basis of the existing methods. However, Statistical Learning Theory, which is specially designed for the case of small samples, supplied a better theoretical frame to the research of Statistical Pattern Recognition under the circumstances of limited number of samples, and presented a new pattern recognition method—Support Vector Meachine (SVM). SVM was presented by Vapnik and his fellows in 1995 as a kind of new mechanic learning method, and was on the basis of Vapnik-Chervonenkis Dimension and Structural Risk Minimization theory. It can solve the problem of small samples non-linear and high dimension pattern recognition. Recently, SVM has been applied in many fields such as face recognition, function approximation and probability density estimation.By the means of SVM, better recognition rate could be acquired in the case that relevantly less training samples has been sampled. At present, many articles about SVM has been published but there are few about how to solve practical problems in the application of projects about SVM. Besides, the selection of parameters of SVM has always been the research direction of many researchers. These problems above will be researched in this thesis.To diagnose turbo-fan engine's gas path fault in test , fault diagnosis system based SVM is established . SVM is special designed for small samples set and can obtain good generalization ability despite of insufficient samples. With the nonlinear model of the engine , the emulated fault data base that contains eight representative gas path faults was built for the engine. SVM is adopted to set up correlation between features and fault patterns and carry out classifier function. The case application in ground test gains commendable results. This method is directive and contributive special for the engine in test.