关键词: 航空发动机;支持向量机;故障诊断;大规模训练集;样本约减;神经网络

Abstract: A new fast multi-class support vector algorithm was presented to solve the multi-class fault diagnosis problem for aero-engine at the condition of large-scale training set. According to the fast algorithm, a hierarchical support vector machine (H-SVM) was chosen for multi-class fault classification. Before SVM training, the training data are first clustered according to their class center Euclid distances with self-organizing feature mapping (SOFM) neural networks. The patterns which have close distances are divided into the same sub-classes to train. This ensures the H-SVM reasonable hierarchical construction and better generalization performance. The relative boundary vectors (RBVs) instead of all the original training samples are used for the training of the binary SVM fault classifiers in H-SVM. This pruning strategy decreases the number of final training sample significantly and can keep classification accuracy almost invariable. Accordingly, the training time is shortened greatly,compared with basic SVM classifier. Meanwhile, owing to the reduction of support vector number, the classification time is also reduced. When a serious sample aliasing exists, the aliasing sample points which are not the same class are eliminated before the relative boundary vectors are computed. Besides, the samples near the relative boundary vectors are selected for SVM training in order to prevent the loss of some key sample points resulted from aliasing. This can improve classification accuracy effectively. A simulation example to classify 5 classes of combination fault of a turbojet engine is finished:the total fault classification accuracy reaches 91.2% and the maximum training time of all the binary SVMs is only 16.20% of basic SVM classifier. When the original training sample number is increased to 7500, the final training sample number after pruning is reduced to 3.05% of basic SVM. Simulation results show that this fast multi-class support vector algorithm is effective, reliable and easy to be implemented for engineering application.

Key words: Aero-engine; Support vector machines; Fault diagnosis; Large-scale training set;Sample pruning ；Neural networks