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

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 straightforward. To solve this issue, this article proposed a transfer learning approach for RUL prediction using a consistency-based regularization. In the proposed deep learning framework, a consistency-based regularization term is added to the objective function to remove the negative effect of missing information in the incomplete target domain dataset. In order to further validate the effectiveness of the proposed method, a comprehensive experimental analysis has been done on two different aerospace and bearing datasets.

Automated summary

Thanks to the successful implementation of intelligent data-driven approaches, predicting the remaining useful life (RUL) problems has gained significant attention. Transfer learning approaches are used to transfer knowledge from source domain data to target domain data. However, there is a discrepancy between the data distribution of source and target domain datasets, which is solved by domain adaptation techniques. This article proposes a transfer learning approach for RUL prediction using consistency-based regularization to handle missing information in the incomplete target domain dataset.