Tianci Zhang

TL;DR: In this paper, a review of the research results on intelligent fault diagnosis with small and imbalanced data (S&I-IFD) is presented, which refers to build intelligent diagnosis models using limited machine faulty samples to achieve accurate fault identification.

TL;DR: A compact convolutional neural network augmented with multiscale feature extraction unit is introduced to extract features at different time scales without adding convolution layers, which can reduce the depth of the network while ensuring classification ability and alleviating the overfitting problem caused by the network being too complicated.

TL;DR: The experimental results on two bearing vibration datasets indicate that the proposed intelligent fault diagnosis method via multimodules gradient penalized generative adversarial networks can not only generate bearing vibration signals but also obtain fairly high fault classificati on accuracy under the small sample condition.

TL;DR: In this paper, a semi-supervised meta-learning network (SSMN) with squeeze-and-excitation attention is proposed for few-shot fault diagnosis, which consists of a parameterized encoder, a non-parameterized prototype refinement process and a distance function.

TL;DR: Experiments show that this iterative matching network augmented with selective sample reuse strategy not only has good performance under varying load and speed conditions but also surpasses other domain adaptation methods.