Health-state assessment is the foundation of optimal-maintenance decision-making for complex systems to maintain reliability and safety. Generating the assessment results in a convincing and interpretable way to avoid potential risks is of great importance. Belief rule base (BRB) as an interpretable model performs well in health-state assessment. However, the interpretability of a BRB-based model may be lost during the optimization process, which is expressed mainly as three problems: expert knowledge is not effectively used in the optimization process; the optimized rules of BRB may be in conflict with real systems; and some parameters get over-optimized, which may affect experts’ initial judgment. Three concepts — “searching intensity”, “interpretability constraint of belief distribution”, and “rule-activation factor” — are defined to address these problems. Using these concepts, we propose a new health-state assessment model based on the interpretable BRB and a new optimization method to improve the accuracy and preserve the interpretability of the new model. To demonstrate the effectiveness of the proposed model, we conducted an aero-engine case study.

健康状态评估是为维护可靠性和安全性而对复杂系统进行最佳维护决策的基础。以令人信服和可解释的方式生成评估结果以避免潜在风险非常重要。信念规则库（BRB）作为可解释的模型在健康状态评估中表现良好。但是，在优化过程中基于BRB的模型的可解释性可能会丢失，主要表现为三个问题：在优化过程中不能有效地利用专家知识；BRB的优化规则可能与实际系统冲突；并且某些参数被过度优化，这可能会影响专家的初步判断。“搜索强度”，“信念分布的可解释性约束”三个概念，定义“规则激活因子”以解决这些问题。使用这些概念，我们提出了一种基于可解释的BRB的新的健康状态评估模型，以及一种新的优化方法，以提高准确性并保留新模型的可解释性。为了证明所提出模型的有效性，我们进行了航空发动机案例研究。