A temporal partial domain adaptation network for transferable prognostics across working conditions with insufficient data

Abstract

Variations in equipment working conditions cause distribution drifts in condition monitoring data, thus impeding the accuracy of remaining useful life (RUL) prediction. Although numerous transfer learning methods have been developed for cross-domain prognostics, it is still challenging to collect enough target domain data to meet training demands because of economic and safety concerns. Therefore, a temporal partial domain adaptation network (TPDAN) is proposed for transferable prognostics across working conditions without run-to-failure data in the target domain. Firstly, the domain discrepancy is appropriately measured by a stage-weighted maximum mean discrepancy (SW-MMD), allowing for the inconsistency of the RUL distribution of available data between domains. Secondly, the ranking-based feature regularization is developed to align the relative position of samples in the feature space and the label space. It facilitates the exploitation of temporal information inherent in the samples and restrains the destruction of the degradation properties of features. By performing domain adaptation based on the above tools, domain-invariant and degradation-aware features can be extracted by the TPDAN to provide reliable knowledge transfer across working conditions. The effectiveness of TPDAN has been validated, and experimental results demonstrate the compelling transferable RUL prediction performance even when run-to-failure data is unavailable in the target domain.