ResFaultyMan:一种智能故障检测预测模型,用于使用无监督学习隔离森林的电力电子系统
Ashkan Safari1, Mehran Sabahi1, Arman Oshnoei2
1Ashkan Safari and Mehran Sabahi with the Faculty of Electrical and Computer Engineering, Tabriz, Iran.
Heliyon
|August 21, 2024
概括
ResFaultyMan是一个无监督的隔离森林模型,增强了电力电子系统 (PELS) 的故障检测. 与现有方法相比,它提供了改进的现实世界的异常检测和诊断.
科学领域:
- 动力电子系统 动力电子系统
- 人工智能的人工智能
- 错误诊断 错误诊断 错误诊断 是一个
背景情况:
- 电力电子系统 (PELS) 越来越复杂,需要先进的故障检测来提高运行可靠性.
- 现有的故障检测方法在现实应用中往往缺乏足够的准确性和诊断能力.
研究的目的:
- 介绍ResFaultyMan,一种基于森林的新型无监督隔离模型,用于PELS的现实世界故障和异常检测.
- 评估ResFaultyMan在各种故障场景中的适应性和有效性.
主要方法:
- 开发了一个无监督隔离森林模型 (ResFaultyMan),利用故障动态进行异常隔离.
- 使用带有负载,Triac开关,电阻,电压源和Pyboard微控制器的测试台进行动态评估.
- 使用Pyboard微控制器实现了Python-to-Python接口,以实现高效的数据传输和高速采样.
主要成果:
- 与OneClassSVM和LocalOutlierFactor相比,ResFaultyMan展示了优越的故障检测能力.
- 绩效使用关键绩效指标 (KPI) 进行评估,包括准确度,精度,回忆和F1评分.
- 该模型显示了在PELS测试环境中适应各种故障场景的适应性.
结论:
- ResFaultyMan提供了一个强大的和有效的解决方案,用于实时检测电源电子系统中的故障.
- 无监督的,以树为基础的方法提供了增强的异常隔离和诊断精度.
- 该研究强调了ResFaultyMan在提高关键PELS应用程序可靠性的潜力.
相关概念视频
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