A Novel Similarity-based Method for Remaining Useful Life Prediction Using Kernel Two Sample Test

Abstract

When abundant historical Run-to-Failure (R2F) data is available, the similarity-based method is one of the favored options for Remaining Useful Life (RUL) prediction due to its simplicity and satisfactory accuracy. In this study, a novel similarity-based methodology for RUL prediction is proposed. The proposed method has two important processing steps: similarity matching and Weibull fitting. The similarity matching screens the historical records by a similarity testing called Kernel Two Sample Test (KTST), and only those records that pass KTST are adopted as references for RUL prediction. For the selected similar records, the RUL is predicted as the remaining time to failure. The Weibull fitting fuses the multiple RUL predictions given by similar historical records. The PDF of RUL is estimated as the fitted Weibull distribution. To demonstrate the effectiveness and superiority of the proposed method, the famous CMAPSS data about aero-engine degradation is adopted for model validation. The results demonstrate improved prediction accuracy comparing with other similarity-based approaches and the state-of-the-art deep learning predictors.