Fault diagnosis of industrial robots based on Adams dynamics simulation

Fault diagnosis is an important part of the intelligent development of industrial robots. Aiming at the problem of lack of data in industrial robot fault diagnosis, this paper introduces a fault diagnosis method based on digital twin and data-driven fusion. The consistency between the model and the actual device is achieved by constructing a digital twin model of the industrial robot and mapping it to the actual industrial robot in real time. In order to solve the problem of lack of data, the fault injection technique was used to inject fault data into the digital twin model and combined with historical data to construct a training dataset. Through simulation experiments on real welding robot data, the machine learning fault diagnosis model was trained and evaluated for precision, recall and F-Score. The experimental results show that this method can effectively solve the problem of lack of fault data and train a reliable fault detection model, providing an effective solution for industrial robot fault diagnosis.