A learning strategy was presented to solve the large-scale support vector machines (SVM) training problem of aero-engine fault diagnosis.According to the strategy

only those samples were retained for final training

which were near the support vector hyperplane of the original small-scale SVM classifier or classified mistakenly by the original classifier.The final pruning set was reduced evidently and the training time was shortened obviously on the condition of high classification accuracy.Meanwhile

the classification time was shortened correspondingly because of the decrease of the support vector number.Something about the parameter selection of the sequential minimal optimization (SMO) method and how to use this potential algorithm in aero-engine fault diagnosis were also discussed.Simulation examples show that this fault diagnosis method proposed for aero-engine

based on SVM with large-scale training set

is effective

reliable and easy to be implemented for engineering application.