A Novel Transfer Learning Approach in Remaining Useful Life Prediction for Incomplete Dataset

Abstract

Due to the successful implementation of intelligent data-driven approaches, these methods are gaining remarkable attention in predicting the remaining useful life (RUL) problems. Within this scope, transfer learning approaches are exploited to transfer the obtained knowledge from the source domain data to the target domain data. Due to the different working regimes and operating conditions, there exists a discrepancy between the data distribution of source and target domain datasets. Domain adaptation techniques are deployed to tackle the data distribution discrepancy. In most prognostic problems, it is assumed that the complete life-cycle run-to-failure information for the target domain dataset is available. However, in real-practical scenarios, providing complete life-cycle data is not s