Remaining useful life (RUL) prediction is essential for ensuring equipment reliability in smart manufacturing. However, mixed-model production introduces a significant challenge due to the discrepancy between the continuous nature of latent degradation and the abrupt, discrete transitions observed in sensor signals. These transitions are driven by the stochastic sequencing of product variants, which obscures the true health state of the equipment. Traditional RUL models are primarily designed for continuous and coherent evolutionary patterns, and consequently, they struggle to distinguish these observable, event-driven jumps from the hidden, underlying degradation trajectories. To resolve this, we propose the Dynamic Segmentation Network (DSNet), a framework designed to synchronize with discrete production rhythms while preserving the continuity of latent health indicators. Specifically, a segmentation loss integrating Proxy-NCA and information entropy is developed to guide the model in discerning discrete process boundaries and achieving semantically consistent partitioning. Furthermore, a hybrid encoding scheme integrates absolute and rotary positional information to capture multi-granularity temporal dependencies, which effectively bridges global degradation trends with local intra-segment structures. These innovations empower DSNet to extract highly discriminative features that are robust to process-induced fluctuations, thereby significantly enhancing RUL prediction performance. Extensive evaluations on 53 industrial welding guns from Bayerische Motoren Werke (BMW) Shenyang plants demonstrate that DSNet achieves reductions in MAE and RMSE by 12.29% and 10.66%, respectively. Consistent performance gains across three public benchmarks further validate the framework’s exceptional generalizability and robustness.
Chen et al. (Mon,) studied this question.