LAUSR

Accurate remaining useful life (RUL) prediction is the key for successful implementation of condition based maintenance program in any industry. Data driven prognostics approaches are generally used to predict the RUL of the components. Presence of noise in the data reduces the accuracy of RUL prediction. Mechanical components are prone to failures due to several failure modes; resulting into multiple failure behaviors or patterns in life test data obtained from various units. If such failure patterns or behaviors are not identified and treated appropriately, the same may act as one of the sources for data noise. In the present research, clustering and change point detection algorithm (CPDA) is used for identification of the presence of multiple failure behaviors in the data. Silhouette width value is used to find out the optimum number of clusters. Combined output of clustering and CPDA is used for developing RUL prediction models. Separate models for single and multiple failure behaviors are constructed using General Log-Linear Weibull (GLL-Weibull) distribution. Results show that identification of failure behavior helps in accurate prediction of RUL. The approach is validated using roller ball bearing life test data.