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Machine performance assessment using Gaussian mixture model (GMM)

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3 Author(s)
Gang Yu ; Mechanical Engineering and Automation, Harbin Institute of Technology (HIT) Shenzhen Graduate School, HIT Campus Xili Shenzhen University Town, Guangdong 518055, China ; Jun Sun ; Changning Li

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.

Published in:

Systems and Control in Aerospace and Astronautics, 2008. ISSCAA 2008. 2nd International Symposium on

Date of Conference:

10-12 Dec. 2008