To solve the problems of insufficient fault-samples and diagnosis-knowledge, and according to the merit of Support Vector Machines (SVM) that can be trained with small-sample, a SVM based unsupervised clustering model is presented. By modifying the decision-function of One-Class Support Vector Machine (1-SVM), which has the ability to find outliers from a dataset without any class of information but rarely is applied to pattern-recognition for its algorithm limits, a Decision-Improved 1-SVM (1-DISVM) is formed. Based on it, multi-pattern training and classing method is designed, then an unsupervised clustering model is constructed. The simulation and diagnostic experiment results of a helicopter’s gearbox show that this clustering model can not only recognize the unknown fault patterns adaptively and precisely, but also learn the nature of the input-patterns from small samples and diagnose the faults successfully.
LIU Xin-min;LIU Guan-jun;QIU Jing;HU Niao-qing. Unsupervised 1-DISVM Based Clustering Model for Fault Diagnosis of Helicopter Gearbox[J]. Acta Aeronautica et Astronautica Sinica, 2006, 27(3): 453-458.
 史铁林,陈勇辉,李巍华,等. 提高大型复杂机电系统故障诊断质量的几种新方法[J]. 机械工程学报,2003, 39(9):1-10. Shi T L, Chen Y H, Li W H, et al. Some measures to improve the quality of fault diagnosis for large-scale complex electromechanical systems [J]. Chinese Journal of Mechanical Engineering, 2003, 39(9):1-10.(in Chinese)  Vapnik V N.统计学习理论的本质[M]. 张学工译.北京: 清华大学出版社, 2000. Vapnik V N. The nature of statistical learning theory[M]. Translated by Zhang X G. Beijing: Qinghua University Press, 2000. (in Chinese)  Batur C,Zhou L,Chan C C. Support vector machines for fault detection. Proceedings of the 41st IEEE Conference on Decision and Control.Las Vegas,Nevada USA, 2002. 1355-1356.  Salat R,Osowski S. Accurate fault location in the power transmission line using support vector machine approach[J]. IEEE Transactions on Power Systems,2004,19(2):979-986.  Schlkopf B,Platt J,Shawe-Taylor J, et al. Estimating the support of a high-dimensional distribution[J]. Neural Computation,2001,13:1443-1471.  Tran Q A,Zhang Q L,Li X. Evolving training model method for one-class SVM. In:IEEE International Conference on Systems, Man and Cybernetics.Washington D C, 2003. 2388-2393.  李先孝. 时间序列分析基础 [M]. 武汉:华中理工大学出版社,1991. Li X X. Analysis base of time serial [M].Wuhan: Science and Industry University of Central China Press,1991.(in Chinese)