MULTI-FAULT DIAGNOSIS FOR

TURBO-PUMP BASED ON MESH
SUPPORT VECTOR MACHINES

YUAN Shengfa^{1, 2}  CHU Fulei¹  HE Yongyong¹

(1. Department of Precision Instruments and Mechanology, Tsinghua University, Beijing 100084;
2. School of Mechanical & Electrical Engineering, Jiangxi University of Science and Technology, Ganzhou 341000 )

Abstract: Support vector machine(SVM) is a new general machine-learning tool based on structural risk minimization principle that exhibits good generalization even when fault samples are few. Fault diagnosis based on support vector machines is discussed. Since basic support vector machines is originally designed for two-class classification, while most of fault diagnosis problems are multi-class cases, a new multi-class classification algorithm named mesh support vector machines is presented to solve the multi-class recognition problems. It is a mesh classifier in which every class constructs two-class SVM classifiers with less than 4 other classes. It is simple and extensible, and has little repeated training amount, so the rate of training and recognition is expedited. The effectiveness of the method is verified by the application to the multi-fault diagnosis for turbo pump test bed.

Key words: Support vector machines Turbo-pump Mesh Multi-class classification

CLC No: V434

国家杰出青年科学基金(50425516)和教育部跨世纪优秀人才培养计划资助项目. Received 20060518, received in revised form 20061228