基于深度学习和激光多普勒测振技术的机械臂电机故障时频尺度诊断方法

机械臂电机振动信号的采集效果较差,影响时频特性分析过程,导致故障诊断效果与精度较差,为此提出基于深度学习和激光多普勒测振技术的机械臂电机故障时频尺度诊断方法。使用激光多普勒测振技术与小波阈值去噪算法,建立机械臂电机振动信号采集系统,获取并重构故障信号;提取电机振动信号的时域、频域等尺度特征,引入人工神经网络建立一个具备学习能力的故障诊断模型,将提取的机械臂电机故障时域、频域等尺度特征输入诊断模型中,输出分类诊断结果,即可完成机械臂电机故障时频尺度诊断。结果表明：利用该方法开展电机故障诊断时,检测结果与实际电机故障类型之间偏差较小,诊断效果好、精度高。

A Time-frequency Scale Diagnosis Method for Mechanical Arm Motor Faults Based on Deep Learning and Laser Doppler Vibration Measurement Technology

The poorer effect of vibration signal collection from robotic arm motors affects the time-frequency character analysis process and leads to lower fault diagnosis effectiveness and accuracy. Therefore, a time-frequency scale diagnosis method for robotic arm motor faults based on deep learning and laser Doppler vibration measurement technology is proposed. Using laser Doppler vibration measurement technology and wavelet threshold de-noising algorithm, we develop a mechanical arm motor vibration signal collection system to obtain and reconstruct fault signals; we extract the time-domain, frequency-domain and other scale features of the motor vibration signal, introduce an artificial neural network to establish a fault diagnosis model with learning ability, input the extracted time-domain, frequency-domain and other scale features of the mechanical arm motor fault into the diagnosis model, and output the classification diagnosis results, which can complete the time-frequency scale diagnosis of the mechanical arm motor fault. The experimental results show that, when this method for motor fault diagnosis is adopted, the deviation between the detection results and the actual motor fault type is small, and the diagnostic results are good with higher accuracy.