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使用机器学习方法检测式感应电机中断裂的转子杆

Lloyd Prosper Chisedzi¹, Mbika Muteba¹

¹Department of Electrical and Electronic Engineering Technology, University of Johannesburg, Auckland Park, Johannesburg 2006, South Africa.

Sensors (Basel, Switzerland)

|November 25, 2023

概括

决策树分类 (DTC) 在检测松鼠感应电机中断裂转子杆 (BRB) 故障方面表现出卓越的性能. 这种机器学习方法为装载和不装载的电机条件提供了高准确度和精度.

科学领域:

电气工程电气工程
机器学习应用机器学习应用
状态监控状态监控

背景情况:

松鼠感应电机在工业应用中至关重要.
断裂转子杆 (BRB) 的故障可能导致重大运行问题和故障.
有效的故障检测方法对于可靠的电机运行至关重要.

研究的目的:

评估和比较用于BRB故障检测的机器学习方法的性能.
评估决策树分类 (DTC),人工神经网络 (ANN) 和深度学习 (DL) 模型的有效性.
确定在各种负载条件下检测BRB故障的最合适方法.

主要方法:

从BRB故障的电机中收集线路电流特征的实验数据.
使用离散里埃变换 (DFT) 来分析频谱的特征提取.
开发和应用DTC,ANN和DL模型用于故障分类.
使用混矩阵验证,使用准确度,精度,回忆和F1分数等指标.

主要成果:

与BRB故障检测的ANN和DL方法相比,DTC表现出更高的准确性和精度.
DTC对电机负载条件的依赖性较小,在没有负载和负载状态都表现良好.
在定子电流中精确检测双频侧带元件是通过DTC实现的.

结论:

DTC是一个非常适合的机器学习候选人,用于检测松鼠感应电机中的BRB故障.
该方法在使用线路电流特征分析的不同负载条件下对电机有效.
DTC为感应电机的预测性维护提供了强大而准确的解决方案.

关键词:

人工神经网络的人工神经网络破碎的旋翼杆故障检测故障检测目前的签名签名当前的签名决策树分类器深度学习是一种深度学习.松鼠的感应电机引擎

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