Degradation manifold dynamic consistency network for probabilistic remaining useful life prediction

Deep learning-based methods for Remaining Useful Life (RUL) prediction generally suffer from insufficient predictive dynamic stability and generalisation capability under real-world industrial conditions. To address this, this paper proposes the Degradation Manifold Dynamic Consistency Network (DMDCN). We initially define a differentiable embedded degradation manifold as the feature representation space. Addressing the limitation that existing embedding methods do not guarantee temporal evolution consistency, a dynamic consistency learning framework is devised to reframe RUL assessment as a joint inference problem of state estimation and dynamics estimation. Through the introduction of the manifold’s local geometric derivatives as the input domain for the dynamics estimator, dynamic consistency between the system’s state representation and its intrinsic evolutionary trend is achieved. Subsequently, this deterministic framework is extended to an approximate Bayesian paradigm, enabling uncertainty quantification via variational inference and Kernel Density Estimation. Empirical results on the public C-MAPSS dataset and a real-world industrial slurry pump dataset indicate that DMDCN improves prediction accuracy and dynamic stability compared to baseline models, validating the method’s potential for applications in high-reliability industrial scenarios.