【摘 要】
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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
【机 构】
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High-Tech Institute of Xi’an,Xi’an 710025,China;Department of Information and Control Engineering,Xi
论文部分内容阅读
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.
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