Dynamics Analysis and Deep Learning-Based Fault Diagnosis of Defective Rolling Element Bearing on the Multi-Joint Robot

Abstract

Industrial robots typically perform a variety of tasks and occupy critical positions in modern manufacturing fields. When certain failures occur in the internal structures of robots, it tends to result in significant financial loss and the consumption of human resources. As a result, timely and effective fault diagnosis is critical to ensuring the safe operation of robots. Deep learning-based methods are currently being widely used by researchers to address some shortcomings of traditional methods. However, due to realistic factor limitations, few researchers take the failure pattern of rotating machinery and the location of fault joints into account at the same time, so the fault types of multi-joint robots are not thoroughly investigated. In this case, we proposed a dynamic simulation method that considers the effects of bearing failures at various faulty joint locations and makes it possible to investigate more possible failure cases of multi-joint robots. In addition, we used LSTM and DCNN to perform staged fault diagnosis tasks, allowing us to gradually locate faulty joints and investigate detailed failure forms. According to the experimental results, distinguishable vibration signals corresponding to various fault states are successfully obtained, and our implemented algorithms are validated for their advanced performance in diagnosis tasks via comparative experiments.