Linearly constrained multi-representation domain generalization for unseen-bearing RUL prediction
TL;DR
The proposed framework improves RUL prediction accuracy and provides competitive cross-bearing generalization and comprehensive experiments show that the proposed framework improves RUL prediction accuracy and provides competitive cross-bearing generalization.
Abstract
Accurate prediction of the remaining useful life (RUL) of rolling element bearings under target-bearing data scarcity remains a critical challenge in prognostics and health management (PHM). This paper proposes a multi-representation domain generalization framework for unseen-bearing RUL prediction. The framework uses a Bidirectional Multi-scale ConvLSTM architecture to capture temporal degradation dependencies and multi-granular spatial patterns from two aligned representations of the same vibration signal: the raw time-domain signal and its continuous wavelet transform (CWT) time-frequency representation. In addition, a lightweight linearly constrained temporal prior is integrated into the prediction layer to encourage a monotonically decreasing degradation trend, following the common damage-irreversibility assumption in bearing prognostics. The target bearing is excluded from training, validation, normalization-parameter estimation, and model selection, and is used only for final testing. Comprehensive experiments on two public benchmark datasets show that the proposed framework improves RUL prediction accuracy and provides competitive cross-bearing generalization.