Abstract:

In condition based maintenance (CBM) using proportional hazards model (PHM), fitting PHM is a very important step because it has a great influence on the effectiveness of the optimal maintenance policy. Previously actual condition monitoring measurements are directly used to fit the PHM. However this may introduce external noise and the optimal maintenance policy obtained based on this model may not be really optimal. To resolve this problem, a data processing method, which is fitting the actual measurements using the Generalized Weibull-FR function, is proposed to remove the external noise and fit the data before using it as input to the PHM. Two case studies using real-world vibration monitoring data are used to demonstrate the proposed approach. The proposed approach is validated to be effective and will save the total average maintenance cost by increasing the average replacement interval and making better use of remaining useful life.