… 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. …

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

… realistic samples that are similar to the real samples, and it can be applied to solve fault
diagnosis problems with insufficient training data, which is called the small sample size condition …

… , 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 …

… , the accuracy of fault diagnosis by using transfer learning … fault diagnosis is difficult to model
under small samples, and … on industrial process fault diagnosis under small samples. The …

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

… 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 …

… parameter for a few-shot samples diagnosis. Although the aforementioned methods made
some breakthroughs in fault diagnosis under small sample size conditions. However, for the …

… a small number of bearing fault samples can be obtained, which leads to an unsatisfactory
effect of traditional fault diagnosis … proposes a small-sample bearing fault diagnosis method …

… has initially shown its advantages in dealing with small sample problems. Thus, the applications
of meta… for S&I-IFD in the extreme case where there are no fault samples available at all. …