Few-shot gearbox fault diagnosed based on meta-learning and time convolution network

In order to resolve the problem that the traditional fault diagnosis methods can not accurately diagnose under complex working conditions and few fault data, a fault diagnosis method based on meta-learning and time convolution network (TCN) is proposed in this paper. Firstly, Ensemble Empirical Mode Decomposition-Independent Component Analysis (EEMDICA) is used to denoise the original signals under different working conditions, and the acquired signals are randomly sampled to form meta tasks according to the meta-learning protocol. Through the TCN model training, the priori knowledge is obtained by optimizing the initialization parameters. The knowledge is used to improve accuracy in unforeseen condition tasks. Finally, simulation experiments are carried out on the gearbox dataset. The results indicate that the method has advantages in solving the fault classification problem of few shot gearbox under complex working conditions.