A model-data combination driven digital twin model for few samples fault diagnosis of rolling bearings

Abstract Deep learning-based fault diagnosis methods for rolling bearings are widely utilized due to their high accuracy. However, they have limitations under conditions with few samples. To address this problem, a model-data combination driven digital twin model (MDCDT) is proposed in this work for fault diagnosis with few samples of rolling bearings. The simulation signals generated by different fault dynamic models of rolling bearings and the measured signals are mixed through MDCDT. The MDCDT generates virtual signals to bridge the gap between the simulated signals and the measured signals by combining their respective advantages. This paper also proposes image coding method based on the Markov transfer matrix (MTMIC) to convert one-dimensional vibration signals into two-dimensional images with both frequency domain information and time domain information, making it easier to extract fault features in neural network training. In the end, the developed MDCDT was evaluated using real rolling bearing data. Experiments show that the MDCDT can generate virtual data for fault diagnosis, and the fault diagnosis accuracy is significantly improved.