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Convolutional Neural Network-Based Stator Current Data-Driven Incipient Stator Fault Diagnosis of Inverter-Fed Induction Motor

机译：基于卷积神经网络的定子电流数据驱动初期定子故障诊断逆变器馈电电动机

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In this paper, the idea of using a convolutional neural network (CNN) for the detection and classification of induction motor stator winding faults is presented. The diagnosis inference of the stator inter-turn short-circuits is based on raw stator current data. It offers the possibility of using the diagnostic signal direct processing, which could replace well known analytical methods. Tests were carried out for various levels of stator failures. In order to assess the sensitivity of the applied CNN-based detector to motor operating conditions, the tests were carried out for variable load torques and for different values of supply voltage frequency. Experimental tests were conducted on a specially designed setup with the 3 kW induction motor of special construction, which allowed for the physical modelling of inter-turn short-circuits in each of the three phases of the machine. The on-line tests prove the possibility of using CNN in the real-time diagnostic system with the high accuracy of incipient stator winding fault detection and classification. The impact of the developed CNN structure and training method parameters on the fault diagnosis accuracy has also been tested.
机译：本文介绍了使用卷积神经网络（CNN）进行感应电动机定子绕组故障的检测和分类的想法。定子匝间短路的诊断推理基于原始定子电流数据。它提供了使用诊断信号直接加工的可能性，这可以取代众所周知的分析方法。对各种水平的定子故障进行了测试。为了评估应用的基于CNN的探测器对电动机操作条件的灵敏度，对可变负载扭矩进行测试，并针对电源电压频率的不同值进行测试。在专门设计的设立的特殊结构上进行了实验测试，该特殊结构的电动机允许在机器的三个阶段中的每一个中的转弯短路的物理建模。在线测试证明了在实时诊断系统中使用CNN的可能性，具有高精度的初始定子绕组故障检测和分类。还测试了发达的CNN结构和训练方法参数对故障诊断精度的影响。

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