Fault Prediction Method Towards Rolling Element Bearing Based on Digital Twin and Deep Transfer Learning
Quanbo Lu, Mei Li
Abstract
Rolling element bearing failure in industrial robots can cause system downtime, high repair costs, and significant economic losses. Traditional fault diagnosis methods assume that training and testing data follow the same distribution, requiring extensive historical data, which is often impractical in dynamic operational environments. Digital twin and transfer learning technologies offer a new approach for intelligent fault diagnosis, addressing these limitations. This paper combines model knowledge and data-driven approaches using digital twin and transfer learning for bearing fault diagnosis. First, a dynamic twin model of the bearing is developed using MATLAB/Simulink (R2018a), simulating fault data under various operating conditions that are difficult to obtain in real-world scenarios. A multi-level construal neural network algorithm is then proposed to minimize cumulative errors in data preprocessing. The digital twin technology generates a balanced dataset for pre-training the model, which is subsequently applied to real-time fault diagnosis in industrial robot bearings via transfer learning, bridging the gap between virtual and physical entities. Experimental results demonstrate the feasibility of the method, with a diagnostic accuracy of 96.95%, marking a 15% improvement over traditional convolutional neural network methods without digital twin enhancement.