基于机器学习的故障分类,以提高感应电机性能
Zawar Ahmed Khan1, Muhammad Amir Raza1, Muhammad I Masud2
1Department of Electrical Engineering, Mehran University of Engineering and Technology SZAB Campus Khairpur Mir's, Sindh, Pakistan.
PloS one
|November 6, 2025
概括
这项研究开发了一种高效的故障分类算法,用于使用机器学习的三相感应电机. 决策树和随机森林模型达到99.95%的准确性,为工业故障检测提供了实用解决方案.
科学领域:
- 电气工程 电气工程
- 机器学习 机器学习
- 工业自动化 工业自动化
背景情况:
- 三相感应电机在工业系统中至关重要.
- 现有的故障检测方法面临着计算成本和准确性方面的挑战.
- 需要高效准确的故障分类算法至关重要.
研究的目的:
- 为三相感应电机设计一个有效的故障分类算法.
- 评估机器学习算法,以准确高效地检测故障.
- 解决现有的计算详尽或低精度方法的局限性.
主要方法:
- 利用了三轴振动电流数据,这些数据来自轴承和转子故障的电机.
- 预先处理的数据使用缺失值的插值和失衡的合成少数人过量采样技术 (SMOTE).
- 通过快速里埃转换 (FFT) 提取频域信息.
- 应用主要组件分析 (PCA) 用于减小维度和SelectKBest用于特征选择.
- 训练并优化了随机森林 (RF),决策树 (DT),k-最近邻居 (KNN) 和极端梯度提升 (XGBoost) 的超参数.
主要成果:
- 决策树和随机森林模型实现了99.95%的准确性.
- k-最近的邻居表现良好,但计算测试成本更高.
- 极端梯度提升实现了87.13%的精度,表明需要进一步优化.
结论:
- 决策树和随机森林模型对于感应电机故障分类非常有效.
- 开发的框架为未来的改进提供了坚实的基础.
- 进一步的研究可以纳入更多的故障类型和功能工程,以改进检测.
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