Mechanical systems hazard estimation using condition monitoring

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.