… establish a diagnosis model for the similar but small target data… machine fault diagnosis
with small sample size. In this paper, we propose a novel fault diagnosis framework for the small …

… to solve fault diagnosis problems with insufficient training data, which is called the small sample
size … However, a single-GAN model cannot achieve a good diagnostic result. To achieve …

… the application of intelligent fault diagnosis into real … problem of small sample. For this
purpose, this paper reviews the related research results on small-sample-focused fault diagnosis …

… By learning smooth labels instead of real labels to alleviate overfitting, so we argue that
LSR has potential advantages in dealing with small samples in fault diagnosis. …

… To improve the accuracy of mechanical fault diagnosis in the case of the small sample,
this article proposes an intelligent fault diagnosis method based on MGPGANs in Fig. 3. The …

… , which is described as small sample problem in this paper. Focus on the small sample
problem, this paper proposes a new intelligent fault diagnosis framework based on dynamic …

… Variational information constrained generative adversarial network is presented for
machine fault diagnosis with a small sample. The proposed approach contains three modules: …

… However, a limited quantity of fault samples is one major character of many fault diagnosis
problems. With a small number of training samples, complex models with a large number of …

… learning theory, meta-learning [20] has initially shown its advantages in dealing with small
sample problems. Thus, the applications of meta-learning theory on S&I-IFD may increase …

… intelligent fault diagnosis with small samples under different working conditions. … fault
diagnosis under small samples, which can utilize one or several samples to solve SSL problems in …