国际设备工程与管理（英文版）2003，Vol.8Issue(3)：179-183,5.
Mechanical Fault Diagnosis Using Support Vector Machine
Mechanical Fault Diagnosis Using Support Vector Machine
摘要
Abstract
The Support Vector Machine (SVM) is a machine learning algorithm based on the Statistical Learning Theory ( SLT) , which can get good classification effects even with a few learning samples. SVM represents a new approach to pattern classification and has been shown to be particularly successful in many fields such as image identification and face recognition. It also provides us with a new method to develop intelligent fault diagnosis. This paper presents a SVM-based approach for fault diagnosis of rolling bearings. Experimentation with vibration signals of bearings is conducted. The vibration signals acquired from the bearings are used directly in the calculating without the preprocessing of extracting its features. Compared with the methods based on Artificial Neural Network (ANN), the SVM-based meth-od has desirable advantages. It is applicable for on-line diagnosis of mechanical systems.关键词
support vector machine (SVM)/fault diagnosis/intelligent diagnosisKey words
support vector machine (SVM)/fault diagnosis/intelligent diagnosis分类
信息技术与安全科学引用本文复制引用
LI Ling-jun,ZHANG Zhou-suo,HE Zheng-jia..Mechanical Fault Diagnosis Using Support Vector Machine[J].国际设备工程与管理（英文版）,2003,8(3):179-183,5.基金项目
This paper is supported by National Natural Science Foundation of China Under Grant No. 50175087. （）
