Multi-feature spaces cross adaption transfer learning-based bearings piece-wise remaining useful life prediction under unseen degradation data

In actual industry, rolling bearings always exhibit complex and uncertain degradation processes, and it is difficult to collect sufficient full lifecycle data, resulting in the remaining useful life (RUL) prognosis performance deterioration. In this study, we propose a novel multi-feature spaces cross adaption transfer learning-based piecewise adaptive RUL prediction method to address these issues. A novel multistage transition point identification method is developed with a combination of adaptive gradient iterative partitioning (AGIP) algorithm and degradation indicator gradient to accurately detect the transition points of each degradation stage. Then, a novel physics degradation rate-informed (PDRI) RUL labeling method is deduced to reflect the realistic of degradation process through calculating the multistage degradation rate. Based on these, the multi-feature spaces cross adaption transformer network (MSCATN)-based piece-wise adaptive RUL prediction model is established to transfer degradation knowledge learned from source domain to unlabeled incomplete target domain. Meanwhile, a joint loss function based on gradient norm balancing is designed to ensure the predicted RUL is consistent with the realistic physics degradation process. Extensive cross-domain transfer prognostics cases were designed on the XJTU-SY dataset to validate the proposed method. Comparison results manifest that the proposed method outperforms other traditional methods and can significantly improve RUL prediction accuracy under unseen degradation data.