Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
560941 | Mechanical Systems and Signal Processing | 2006 | 13 Pages |
Abstract
The ability to predict hazards of mechanical systems accurately can significantly enhance the predictive maintenance task. However, predicting hazards of systems accurately is non-trivial, especially when historical failure data are sparse or zero. The proposed proportional covariate model (PCM) overcomes this difficulty. This paper describes the concepts of PCM briefly and focuses on the estimation of the hazards of mechanical systems using accelerated life tests and condition monitoring data. This new approach to hazard estimation can reduce the number of accelerated life tests significantly. The hazard estimation can further be refined and updated with on-line condition monitoring data on a continual basis.
Related Topics
Physical Sciences and Engineering
Computer Science
Signal Processing
Authors
Yong Sun, Lin Ma, Joseph Mathew, Wenyi Wang, Sheng Zhang,