Li Shuozi, Liu Mingyuan, Ma Ning, He Wei
School of Computer Science and Information Engineering, Harbin Normal University, Harbin, 150025, China.
Sci Rep. 2025 Jan 22;15(1):2852. doi: 10.1038/s41598-025-85792-8.
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.
随着复杂系统长时间运行，其健康状况持续下降，因此评估复杂系统的健康状态很重要。置信规则库（BRB）作为一种能有效处理不确定性且具有可解释性的半定量方法，在复杂系统健康状态评估领域得到广泛应用。在实际工程中，BRB仍存在问题：专家知识的不完整性以及各专家认知能力的不一致性会对模型构建和可解释性产生影响。为解决这一问题，提出了一种基于联合多专家置信规则库（BRB-ME）的复杂系统健康状态评估方法。专家首先构建自己的模型，并设计了一种新的多专家知识融合算法用于融合不同专家模型。将证据推理（ER）用作模型的推理机。接下来，使用具有多专家知识约束的多种群进化鲸鱼优化算法（C-MEWOA）对BRB-ME模型进行优化。最后，通过锂离子电池和飞轮的案例研究验证了BRB-ME模型在健康状态评估中的有效性。对比研究表明，BRB-ME模型能够融合多专家知识，在评估结果的稳定性和准确性方面具有优势。
