通过故障模式和自我评估决策算法对电气设备的数据固有的评估
A Aranizadeh1, B Vahidi2, A Khorsandi1
1Department of Electrical Engineering, Amirkabir University of Technology, Tehran, 1591634311, Iran.
Scientific reports
|November 27, 2025
概括
本研究介绍了一种自我评估决策算法 (SEDMA),用于准确评估电力系统设备状况. 通过分析历史数据的磨损和故障模式,SEDMA 提高了以可靠性为中心的维护.
科学领域:
- 电气工程 电气工程
- 电力系统工程 电力系统工程
- 可靠性工程可靠性工程
背景情况:
- 电力系统的可靠性取决于准确的设备状况评估.
- 不确定,杂和倾向于异常的测量数据会影响分析和维护决策.
- 现有的方法可能缺乏可靠条件评估的自我参考能力.
研究的目的:
- 为设备状况评估提出一种新的自我评估决策算法 (SEDMA).
- 为了使设备能够根据历史行为自我评估状态,减轻对外部参考的依赖.
- 提高电力系统的维护决策的准确性和可靠性.
主要方法:
- 开发了一个整合模糊逻辑和基于的降解建模的框架.
- 实施了改进的数据预处理阶段,用于检测和删除异常值.
- 利用自我比较过程量化设备磨损和故障模式.
主要成果:
- 在断路器数据上,SEDMA展示了稳定和可解释的状态评估结果.
- 在参数灵敏度分析下达到不到5%的变化,表明了强度.
- 在稳定性和准确性方面表现优于统计和贝叶斯基线.
结论:
- SEDMA为条件评估提供了一个具有物理意义和自我适应的框架.
- 该算法表现出线性计算复杂性,适合实时大规模监控.
- SEDMA 增强了电力系统的以可靠性为中心的维护策略.
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