Prognostics and Health Management
In the safety critical systems such as aerospace structures and nuclear power plant, it is utmost important to monitor the performance degradation during their operation arising due to the wear and crack growth. Current practice is just to rely on periodic maintenance regardless of their criticality, which is overly costly and time consuming. In order to resolve this problem, PHM (prognostics and health management) is recently drawing attention in the associated industry. The PHM is to monitor the health signal of the system under operation, diagnose the state of health, and predict the residual life under the future operation. The PHM technique may enable near-zero break down of the system, and hence, the condition based maintenance with cost-effective way. In this lab, the model based prognostics technique is developed for crack growth of aircraft, battery degradation and damage growth of wind turbine gearbox. During the process, Bayesian framework is employed to account for the various uncertainties.
