Machinery Multimodal Uncertainty-Aware RUL Prediction: A Stochastic Modeling Framework for Uncertainty Quantification and Informed Fusion

Abstract

Accurate prediction of machinery’sremaining useful life (RUL) is essential for preventing catastrophic breakdowns and supporting predictive maintenance. Although RUL prediction has been extensively studied, most literature develops on unimodal data, which providesa limited and often biased perspective. Multimodal monitoring, which collects multiple sensor data, enables a more comprehensive understanding of degradation processes. While promising, significant challenges are encountered in existing methods: 1) point yet deterministic predictions are predominantly produced which, while potentially erroneous, tend to exhibit overconfidence, thereby lacking the dynamic uncertainty informing; 2) the processing of heterogeneous data and the achievement of physically interpretable fusion remain challenging; and 3) anomalies in the operation process are not appropriately identified. To address these issues, a new multimodal uncertainty-aware RUL prediction framework is proposed, grounded in stochastic modeling. Fractional stochastic differential equation-controlled subnets process each modality independently, wherein layer-wise transformations are modeled as state evolution in stochastic dynamical systems, allowing modality-specific uncertainty to be quantified without requiring parameter priors. A Lagrange multiplier-based fusion module is subsequently employed to perform explicit uncertainty-based fusion, enabling an interpretable and synergistic integration. Validation on harmonic drive reducers for robots demonstrates the superiority of the proposed framework, achieving an average improvement of 26.6% in RMSE and a 16.6% reduction in MAPE compared to state-of-the-art benchmarks. Furthermore, the method significantly reduces prediction uncertainty variance by 21.3%, offering more reliable insights into system degradation.