Remaining useful life estimation using deep metric transfer learning for kernel regression

Accurate estimation of remaining useful life (RUL) is indispensable for the safe operation of rotating machinery, reducing maintenance costs and unnecessary downtime. Numerous data-driven models have been reported to predict the RUL of bearings using historical data. However, it is still very challenging to predict the RUL of bearings under different operating conditions. It is necessary to propose a model which can extract domain invariant deep features and accurately predict the RUL of bearings under new operating condition. In this paper, a novel method called deep transfer metric learning for kernel regression (DTMLKR) is proposed and applied to the RUL prediction of bearings under multiple operating conditions. This method combines deep metric learning with transfer learning (TL) to solve regression problems. Case studies on the IEEE PHM Challenge 2012 dataset demonstrate the effectiveness of the proposed method. Compared with other state-of-the-art methods, the superiority of the proposed method is verified.

Automated summary

The paper proposes a novel method called deep transfer metric learning for kernel regression (DTMLKR) for predicting the RUL of bearings under multiple operating conditions. This method combines deep metric learning with transfer learning to solve regression problems and has shown effectiveness in case studies. The superiority of the proposed method compared to other state-of-the-art methods is verified through experiments on the IEEE PHM Challenge 2012 dataset.