Intelligent Joint Actuator Fault Diagnosis for Heavy-Duty Industrial Robots

A data-driven intelligent fault diagnosis algorithm is designed in this article for heavy-duty industrial robots (IRs), which aims to accurately detect and identify joint actuator faults that may occur during the operation of industrial heavy-duty robot arms. Considering the fact that faulty samples of IRs are difficult to access, a simulation model-based strategy is adopted in this article. With the help of Euler–Lagrange method, dynamics of heavy-duty IRs are established with considering the joint flexibility. By injecting fault in different joint actuators, unbalanced normal and faulty samples are obtained, based on which an intelligent diagnosis model is constructed. Subsequently, a composite neural network model, long short-term memory (LSTM)-convolutional neural network (CNN), is proposed, which combines the merits of LSTM network and CNN. The constructed LSTM-CNN model is then trained and validated using generated data to achieve fault diagnosis and identification of actuators of heavy IRs. Finally, the constructed intelligent fault diagnosis model is experimentally validated, and the result analysis demonstrates that the proposed algorithm shows superior accuracy and precision in diagnosing single-joint and multiple-joints faults.