基于自适应证据推理规则的复杂机电系统的性能退化评估方法
Bangcheng Zhang1, Shuo Gao2, Shiyuan Lv2
1School of Mechanical and Electrical Engineering, Changchun University of Technology, Changchun 130012, China; School of Mechanical and Electrical Engineering, Changchun Institute of Technology, Changchun 130103, China.
ISA transactions
|November 26, 2024
概括
本研究引入了适应性证据推理 (AER) 规则,以动态调整复杂系统中的指标权重. AER规则有效地处理了随时间和条件变化的重要性,改善了性能退化评估.
科学领域:
- 工程 工程师 工程师 工程师
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 传统的证据推理 (ER) 规则与动态系统作斗争,指标的重要性随着时间的推移和不同的条件下发生变化.
- 现有的ER方法缺乏适应性,无法应对复杂的机电系统中的性能退化评估等任务.
研究的目的:
- 提出适应性证据推理 (AER) 规则,能够根据时间和工作条件动态调整指标权重.
- 解决传统ER规则在处理时间变化和条件依赖数据方面的局限性.
主要方法:
- 开发了一个适应性证据推理 (AER) 规则,用于时间和工作条件的适应性权重操作.
- 使用CMA-ES算法优化AER模型参数.
- 通过两个案例研究验证了AER规则:计算机数控 (CNC) 实验和轮风扇航空发动机模拟.
主要成果:
- AER规则证明了有效的动态重量调整用于性能降低评估.
- 案例研究证实了AER规则在处理不断变化的系统动态方面优于传统方法的优势.
- 该方法被证明是有效和实用的,用于现实世界的应用.
结论:
- 拟议的自适应证据推理 (AER) 规则为分析具有不确定性和时间变化的数据的动态系统提供了强大的解决方案.
- 在复杂的电机系统中,AER显著提高了性能退化评估的准确性和可靠性.
- AER规则为基于证据的决策中的动态权重调整提供了一种实用和有效的方法.
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