基于多专家共同信念规则基础的复杂系统的健康状况评估方法
Shuozi Li1, Mingyuan Liu1, Ning Ma2
1School of Computer Science and Information Engineering, Harbin Normal University, Harbin, 150025, China.
Scientific reports
|January 22, 2025
概括
评估复杂系统健康的新方法,联合多专家信念规则基础 (BRB-ME),有效地融合专家知识. 这种方法提高了关键系统的健康状况评估的稳定性和准确性.
科学领域:
- 工程 工程师 工程师 工程师
- 人工智能的人工智能
- 系统健康管理 系统健康管理
背景情况:
- 复杂的系统随着时间的推移而退化,需要准确的健康状况评估.
- 信念规则基础 (BRB) 是管理健康评估不确定性的宝贵工具,但在专家知识整合方面面临挑战.
- 不一致的专家认知和不完整的知识阻碍了传统的BRB模型构建和解释性.
研究的目的:
- 为复杂系统提出一种新的健康状况评估方法,使用联合多专家信念规则基础 (BRB-ME).
- 在BRB建模中解决专家知识完整性和认知一致性的局限性.
- 提高复杂系统健康评估的准确性和稳定性.
主要方法:
- 专家们单独构建他们的BRB模型.
- 开发了一种新的多专家知识融合算法,以整合各种专家模型.
- 证据推理 (ER) 方法作为推理引擎.
- 一个受约束的多种群进化优化算法 (C-MEWOA) 用于优化BRB-ME模型.
主要成果:
- BRB-ME模型成功地融合了多位专家的知识.
- 关于离子电池和飞轮的案例研究证明了该模型的有效性.
- 对比分析证实了BRB-ME模型在健康状况评估中的卓越稳定性和准确性.
结论:
- 拟议的BRB-ME方法有效地整合了复杂系统健康评估的多专家知识.
- 该方法克服了传统的BRB方法在专家知识方面的局限性.
- BRB-ME在评估稳定性和准确性方面提供了显著的改进,并得到了实际应用的验证.
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