Condition Diagnosis for Rotating Machinery Using Support Vector Machines and Symptom Parameters in Frequency Domain
Up to now, many condition diagnosis methods based on the traditional artificial intelligence, such as neural networks (NN), genetic algorithms (GA), etc, have been proposed in the field of condition diagnosis for rotating machinery. These methods depend on the assumption that the number of samples tends to infinity, and also require a large amount of training samples and highly sensitive symptom parameters (SPs). However, as the satisfied samples cannot be easily acquired from a real plant and SPs are not so highly sensitive as supposed to be. In many cases of condition diagnosis for rotating machinery, the intelligent methods, such as neural networks, genetic algorithms, etc., often cannot converge when learning. In order to solve these problems, a new condition diagnosis method using support vector machines (SVWs) is proposed in this paper. The practical examples of diagnosis for rotating machinery are shown to verify the efficiency of the proposed method.
support vector machines discrimination index optimal hyper-plane quadratic problem input space feature space kernel function
Hongtao XUE Peng CHEN
Graduate School of Bioresources, Mie University 1577 Kurimamachiya-cho, Tsu-shi, Mie, 514-8507, Japan
国际会议
深圳
英文
75-78
2011-03-28（万方平台首次上网日期，不代表论文的发表时间）