一个混合的DS组合规则,基于诱导顺序加权平均操作员和自适应加权
Zhang Maoyun1, Xi Huizhuang2, Tang Chen2
1Faculty of Mechatronic Engineering, Changchun University of Science and Technology, Changchun University of Science and Technology, ChangChun, JiLin, China. zhmaoyun@cust.edu.cn.
这项研究引入了一种新的混合DS (Dempster-Shafer) 证据组合规则,以应对融合高冲突数据的挑战. 新方法提高了故障诊断和信息融合任务的准确性,优于现有的方法.
科学领域:
- 人工智能的人工智能
- 信息融合 信息融合
- 决策理论 决策理论
背景情况:
- 斯特-沙弗 (D-S) 证据理论对于多源信息融合,故障诊断和安全评估至关重要.
- 在DS理论中结合高冲突证据带来了重大挑战,往往导致不合理的结果.
研究的目的:
- 提出一种新的混合DS组合规则,旨在有效处理高冲突证据.
- 提高信息融合和故障诊断系统的准确性和可靠性.
主要方法:
- 开发了一个混合的DS组合规则,集成了诱导顺序加权平均 (IOWA) 运算符和自适应加权.
- 通过数值示例验证了该方法,并将其应用于地铁波吉轴承故障诊断.
主要成果:
- 拟议的方法在高冲突 (高达71.6%) 和低冲突 (高达67.8%) 场景中,与现有方法 (Dempster,Sun,Abelan,Murphy,Tao,Li) 相比,在准确性方面取得了显著的改进.
- 与基准方法相比,在诊断地铁托盘轴承故障方面取得了更高的准确性 (外环:高达63.9%,内环:高达62.5%).
- 拟议规则的计算时间与传统的DS理论和其他方法相比,优于Abellan和Li方法.
结论:
- 拟议的混合DS组合规则为融合高冲突证据提供了强大而准确的解决方案.
- 该方法在诸如故障诊断等复杂应用中显示出实际优势,特别是在机械系统中.
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