Deep transfer learning-based hierarchical adaptive remaining useful life prediction of bearings considering the correlation of multistage degradation

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The multi-stage degradation process of bearings significantly affects the predicted performance of rotating machinery and equipment in long-term operation. However, the inherent correlations between degradation stages, and domain-invariant degradation features of multistage that affect the remaining useful life (RUL) are mostly ignored, leading to RUL prognostics deterioration under cross-working conditions. Therefore, a novel deep transfer learning-based hierarchical adaptive RUL prediction approach is applied to overcome this problem. Firstly, a novel multistage degradation (MD) division method is proposed with a combination of maximum mean discrepancy and statistical process analysis to accurately obtain the varied health indicators (HIs) with MD without setting any threshold. Then, the obtained MD features are fed into the residual network to automatically recognize the HIs and MD in response to multi-variability trend changes. Finally, a novel hierarchical adaptive RUL model based on a deep adaptive-transformer is proposed to transfer the learned domain-invariant degradation features of multistage from one to various working conditions. Meanwhile, hierarchical adaptive tuning (HAT) reduces the RUL model loss to guarantee a more accurate prediction of each degradation stage. To validate the proposed approach, extensive experiments were conducted in seven cross-domain cases on the XJTU-SY dataset to predict RUL. Comparison results indicate that the proposed approach is more accurate than other conventional methods because it considers the RUL of bearing at each stage.