使用同步多传感器数据收集的三相感应电机的综合故障诊断
Kevin Thomas1, Ahasanur Rahman1, Wesam Rohouma2
1Department of Electrical Engineering, College of Engineering, Qatar University, Doha, Qatar.
Scientific data
|August 22, 2025
概括
一个新的数据集捕获了三相感应电机故障诊断的同步振动,电压和电流数据. 这种资源可以实现先进的机器学习,用于预测性维护,通过随机森林模型实现99.82%的准确性.
科学领域:
- 电气工程
- 机械工程
- 数据科学
背景情况:
- 感应电机对于工业至关重要,但容易发生机械和电气故障.
- 有效的故障诊断对于操作可靠性和预测性维护至关重要.
- 现有的数据集往往缺乏同步的多传感器数据,限制了全面的分析.
研究的目的:
- 引入一个新的,同步的多传感器数据集用于三相感应电机故障诊断.
- 为开发和验证先进故障检测算法提供丰富的资源.
- 促进用于运动健康监测的机器学习研究.
主要方法:
- 收集0.2千瓦引擎的振动,电压和电流的同步实时数据.
- 使用高分辨率的传感器,采样频率为50kHz.
- 模拟故障场景,包括移除相位和机械失调.
- 组织数据成十个CSV文件, 代表不同的操作状态.
主要成果:
- 在数据集上训练的随机森林分类器在故障诊断中获得了99.82%的准确性.
- 同步的电气和机械数据使得先进的交叉传感器故障分析成为可能.
- 证明了数据集适用于实时故障诊断和预测性维护.
结论:
- 新的数据集是推进诱导电机故障诊断的宝贵资源.
- 同步的多传感器数据显著提高了机器学习模型的能力.
- 该数据集支持对工业发动机的强有力的预测性维护策略的开发.
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