A Causality-Informed Correlation-Aware Health-State Assessment for Complex Equipment

Health-state assessment is a critical component of prognostics and health management (PHM) for complex equipment. Previous studies on assessing the health state of complex equipment have overlooked the statistical dependence arising from causal coupling relationships between subsystems, which is defined as causality-informed correlation in this study. This correlation introduces redundancy in health information, leading to assessment bias. To address these limitations, this study proposes a health-state assessment model based on the evidential reasoning rule considering causality-informed correlation (ERr-CIC). First, the causal coupling relationships in dynamics and their effects on health-assessment results are analyzed. Based on this analysis, the convergent cross-mapping (CCM) method is employed to examine causal coupling between subsystems. Subsequently, a health-assessment model based on ERr-CIC is developed. This model incorporates a discount factor to quantify the causality-informed correlation among indicators, realized using a conditionally hybrid correlation coefficient (CHCC), and a fusion order derived from signaling sequences. Furthermore, a sensitivity and robustness analysis of the model output to the CHCC is conducted to identify the key parameters governing system behavior and to assess the reliability of the model results under parameter perturbations. Finally, experiments are performed on the PAMD simulation device for validation, and the proposed model is compared with three other typical health-state assessment models. The results show that the ERr-CIC model proposed in this paper achieves relatively balanced performance in terms of stability and interpretability while maintaining competitive model accuracy.