Machine Performance Assessment Using Gaussian Mixture Model (GMM)

摘要：

In this paper, we present a simple and efficient machine performance assessment approach based on Gaussian mixture model (GMM). By only utilizing the machine performance signatures generated from normal machine operation, a GMM can be trained to model the underlying density distribution of the training data. Machine performance assessment can be accomplished by quantifying the distance between the GMM for the most recent observed machine condition and that for normal machine operation. Experimental results based on real industrial run-to-failure bearing tests have shown that GMM can efficiently assess the performance of test bearings. The proposed approach has a great potential for a variety of machine performance assessment applications.

作者: Gang Yu Jun Sun Changning Li

作者单位: Mechanical Engineering and Automation Harbin Institute of Technology (HIT) Shenzhen Graduate School HIT Campus Xili Shenzhen University Town Shenzhen,Guangdong 518055,P.R.China

会议类型: 国际会议

会议名称: The 2nd International Symposium on Systems and Control in Aeronautics and Astronautics(第二届航空航天系统与控制国际会议 ISSCAA 2008)

会议地点: 深圳

会议语种:英文

页码: 258-262

在线出版日期: 2008-12-10（万方平台首次上网日期，不代表论文的发表时间）