Hydraulic System Faults Diagnosis Based on Multi-class Support Vector Machine

摘要：

There Is no sufficient evidence on classification, because of lack of hydraulic system fault samples. The classification results with definite guess are not exactly right Meanwhile, there are many types of hydraulic system faults, but present classifiers can only classify two-class problems, which are not fit for hydraulic system faults diagnosis. In order to solve the preceding problems, a method for hydraulic system faults diagnosis based on multi-class support vector machine (MSVM) is proposed. A support vector machine (SVM) has strong classification ability with fewer samples taker. For k -class problem of hydraulic system, it combines k(k-l)/2 two-class SVM classifiers, one for each pair of classes. The experimental results indicate that this method is a more effective and feasible tool for hydraulic system faults diagnosis than Neural Net

关键词： hydraulic system fault diagnosis support vector machine

作者: Li Sheng Zhang Peilin Wang Guode

作者单位: Department 1st, Ordnance Engineering College Shijiazhuang, 050003, China

会议类型: 国际会议

会议名称: 2010 International Conference on Digital Manufacturing and Automation(2010 数字制造与自动化国际会议 ICDMA 2010)

会议地点: 长沙

会议语种:英文

页码: 811-813

在线出版日期: 2010-12-18（万方平台首次上网日期，不代表论文的发表时间）