Study of Data Fusion Method for Fault Diagnosis Based On FDR Feature Selection Algorithm and HMM/SVM Model

摘要：

　　To effectively avoid the loss of useful information,in this paper,we extract feature information from the fault signal of rotating machinery in different aspects such as amplitude-domain,time-domain and time-frequency domain.Then for the multi-dimensional feature extraction is prone to the problem ofdime nsion disaster,introduce the principles of FDR in data mining to determine the classification ability of each individual feature,and introduce the cross correlation coefficient to solve the problem that dealing with individual feature neglects the interrelationship between the features,and construct a new feature level data fusion algorithm.Finally,According to the characteristics of the HMM (Hidden Markov model),SVM (Support Vector Machine) and its hybrid model,we construct a new decision-level data fusion model.

关键词： Fault diagnosis Support Vector Machine Hidden Markov model Data Fusion Fishers Discriminant Ratio

作者: Sheng LI Chun-liang ZHANG Liangbin HU

作者单位: School of Mechanical Engineering,University of South China,Hengyang,421001,China School of Mechanical and Electrical Engineering,Guangzhou University,Guangzhou,Guangdong,510006,Chin

会议类型: 国际会议

会议名称: the 2012 International Conference on Manufacturing Engineering and Automation (2012年制造工程与自动化国际会议(ICMEA2012))

会议地点: 广州

会议语种:英文

页码: 2046-2050

在线出版日期: 2012-11-16（万方平台首次上网日期，不代表论文的发表时间）