Health status assessment is an important measure for maintaining the safety of spacecraft flywheel systems. The influence of noise, sensor quality, and other disturbance factors can lead to a decrease in the reliability of the collected information. This can affect the model accuracy. Moreover, a loss of belief in the model is frequently caused by the opaque nature of the procedure and the incomprehensibility of the outcomes, particularly in fields such as aerospace. It is urgent to maintain the interpretability of the model and successfully identify the unreliability of the observed data. Therefore, this paper proposes a spacecraft flywheel system health status assessment method under perturbation based on interpretable belief rule base with attribute reliability (IBRB-r). First, the attribute reliability is calculated based on the average distance method, and a new fusion method of attribute reliability is proposed to reduce the interference of unreliable information. Then, a new interpretable constraint strategy is proposed to improve the rationality and interpretability of the parameters. Finally, the proposed method is validated by a case study of the health status assessment of a spacecraft flywheel system. Experiments show that the IBRB-r maintains high accuracy and interpretability under unreliable observation data.

When making maintenance decisions for diesel engines, the safety assessment plays a crucial role. Belief rule base (BRB) is a rule-based model embedded in expert knowledge with the advantage of being interpretable. However, due to the complexity of diesel engine systems and the impact of potential failures, their safety assessment faces the following challenges: (1) The interpretability of BRBs is often weakened during the building and optimization process. (2) Existing BRB models for safety assessment lack effective interpretability quantification methods. To address the above challenges, a diesel engine safety assessment method based on an interpretable BRB with the metric is proposed in this paper. First, an interpretability metric method for the interpretable BRB model for diesel engine safety assessment is proposed to quantify the interpretability of the entire modeling process. Second, a new statistical-based method for calculating the credibility of expert knowledge is proposed. Third, on the basis of these, three new interpretability maintenance strategies are designed to correct behaviors that destroy the interpretability of the model. The validity of the proposed model is verified through a case study of the safety assessment of a WD615 diesel engine.

The health of complex systems continues to decline as they operate over long periods of time, so it is important to assess the health state of complex systems. Belief rule base (BRB) is widely used in the field of health state assessment of complex systems as a semi-quantitative method that can address uncertainty effectively and with interpretability. In practical engineering, BRB still has problems: the incompleteness of expert knowledge and the inconsistency of the cognitive abilities of each expert have an effect on the construction of the model and interpretability. To address this problem, a complex system health state assessment method is proposed based on a joint multiexpert belief rule base (BRB-ME). Experts first build their own models, and a new multiexpert knowledge fusion algorithm is designed for the fusion of different expert models. The ER is used as the inference machine for the model. Next, a multi-population evolution whale optimization algorithm with multiexpert knowledge constraints (C-MEWOA) is used to optimize the BRB-ME model. Finally, the effectiveness of the BRB-ME model in health state assessment is verified through case studies of lithium-ion batteries and flywheels. Comparative studies have shown that the BRB-ME model can fuse multiexpert knowledge and has advantages in terms of the stability and accuracy of assessment results.

New energy vehicles (NEVs) are a key technology for achieving the low-carbon transition of the transportation sector. The operational safety and energy efficiency of NEVs are critical for the sustainable development of transportation systems. However, the complex system structure and highly variable operating conditions make health state assessment of NEVs challenging. The belief rule base (BRB) framework combines knowledge-driven and data-driven information to handle uncertainty and has been widely used in health assessment of complex systems. In multi-attribute multi-fault scenarios, BRB models still suffer from combinatorial rule explosion and cognitive bias from a single expert, which lead to increased model complexity and reduced interpretability. To address these issues, this study develops an ensemble BRB method with dual-adaptive feature selection, termed Dual-Adaptive Ensemble BRB (DA-EnBRB). The method employs a dual-adaptive mechanism based on Extreme Gradient Boosting (XGBoost) and mutual information (MI) to dynamically construct compact multi-attribute feature subspaces and to build a BRB expert model in each subspace. A multi-strategy crowned porcupine optimizer (MSCPO) optimizes the parameters of all BRB experts. Class-specific expert weights are then learned and used to fuse sub-model outputs via weighted voting, yielding the final diagnostic result. Experiments on NEV fault diagnosis and NEV energy efficiency assessment demonstrate the effectiveness and generalization capability of DA-EnBRB.