Fault Diagnosis of Motor Bearing Based on Current Bi-Spectrum and Convolutional Neural Network

Abstract Motor bearings are prone to different degrees of performance degradation, fatigue damage and failure undergoing complex and harsh environments. Vibration signal analysis is a mature method for diagnosing motor bearing faults, while it is not applicable for installing additional vibration sensors on many occasions. Practically, the fault of motor bearings changes the air gap flux between the rotor and stator, which leads to harmonic fluctuations in the stator current. The current signals can be used to diagnose the motor bearing faults without additional sensors. Inevitably the harmonics caused by the motor bearing faults will be coupled with the original signals. This paper combines bi-spectrum and Convolution Neural Network (CNN) to analyze the current signals of motor bearing faults. The CNN diagnosis model is trained based on the local bi-spectrum of current, and the CNN parameters are optimized. Diagnose and analyze motor bearing faults with different fault implantation methods, working conditions, fault degrees and fault locations. The diagnostic accuracy reaches more than 80%.摘要 电机轴承在复杂严苛的运行环境中，极易出现不同程度的性能衰减、疲劳损伤乃至失效故障。振动信号分析法是电机轴承故障诊断的成熟技术，但在多数应用场景中无法额外加装振动传感器，难以实际落地。实际工况下，电机轴承故障会改变定转子间的气隙磁通，进而引发定子电流产生谐波波动，因此无需额外加装传感器即可通过电流信号实现电机轴承故障诊断。然而，电机轴承故障引发的谐波不可避免地会与原始电流信号相互耦合。本文将双谱（bi-spectrum）分析法与卷积神经网络（Convolution Neural Network, CNN）相结合，对电机轴承故障的电流信号开展分析：基于电流信号的局部双谱特征训练卷积神经网络诊断模型，并优化其网络参数；针对采用不同故障植入方式、运行工况、故障程度与故障位置的电机轴承故障展开诊断与分析，最终诊断准确率可达80%以上。