Research on Robot Electrical Fault Detection and Diagnosis Based on Deep Learning
Robots are widely used in manufacturing and related fields due to their efficient and high-intensity operation methods.However,when robots malfunction,it often leads to production line stagnation,wasting a lot of manpower and material resources,and even endangering the safety of workers.Traditional fault diagnosis methods are time-consuming,inefficient,and have low accuracy in fault identification.Based on deep learning theory,this article establishes a fault diagnosis model suitable for industrial robots by analyzing the vibration characteristics of various joints and actuators of robotic arms.The accuracy of electrical fault detection and diagnosis is analyzed using the ABBirb120 robot as an example.The results show that when the number of iterations reaches 900 or more,the accuracy of fault identification tends to 99.4%.
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