A model to predict the residual life of rolling element bearings given monitored condition information to date

In condition monitoring practice, one of the primary concernsof maintenance managers is how long the item monitored can survivegiven condition information obtained to date. This relates tothe concept of the condition residual time where the survivaltime is not only dependent upon the age of the item monitored,but also upon the condition information obtained. Once sucha probability density function of the condition residual timeis available, a consequencial decision model can be readilyestablished to recommend a ‘best’ maintenance policybased upon all information available to date. This paper reportson a study using the monitored vibration signals to predictthe residual life of a set of rolling element bearings on thebasis of a chosen distribution. A set of complete life dataof six identical bearings along with the history of their monitoredvibration signals is available to us. The data were obtainedfrom a laboratory fatigue experiment which was conducted underan identical condition. We use stochastic filtering to predictthe residual life distribution given the monitored conditionmonitoring history to date. As the life data are available,we can compare them with the prediction. The predicted resultsare satisfactory and provide a basis for further studies. Itshould be pointed out that although the model itself is developedfor the bearings concerned, it can be generalized to modellinggeneral condition-based maintenance decison making providedsimilar conditions are met.