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 diagno-sis method for robotic arm motor faults based on deep learning and laser Doppler vibration measurement technology is pro-posed.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 mo-tor fault type is small,and the diagnostic results are good with higher accuracy.
deep learning networklaser Doppler vibration measurement technologymechanical armmotor failure
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