Most intelligent fault diagnosis methods of rotating machinery generally consider that normal
samples and fault samples as equally important for pattern recognition training. It ignores …

Identifying fault of rotating machinery under different load conditions with high accuracy is a
remaining challenge for vibration signal based fault diagnosis. Aiming at this challenge, this …

With evaluating the randomness and the nonlinear dynamic change of time sequence
effectively, multiscale fuzzy distribution entropy (MFDE) is proposed to extract fault features …

Fault diagnosis methods based on dimensionless indicators have long been studied for
rotating machinery. However, traditional dimensionless indicators frequently suffer a low …

Selecting the most discriminative features from the original high dimensional feature space
and finding out the optimal parameters for recognition model both have vital influences on …

Feature extraction is one of the most important, pivotal, and difficult problems in mechanical
fault diagnosis, which directly relates to the accuracy of fault diagnosis and the reliability of …

In view of the limitations of existing rotating machine fault diagnosis methods in single-scale
signal analysis, a fault diagnosis method based on multi-scale permutation entropy (MPE) …

A novel rolling bearing fault diagnosis method based on hierarchical fuzzy entropy (HFE),
Laplacian score (LS) and improved support vector machine based binary tree (ISVM-BT) is …

Using transfer learning (TL) for fault detection and diagnosis has been a hot topic in
prognostic and health management (PHM) field. In this article, a systematic framework is …

Extracting the failure related information from vibration signals is a very important aspect of
vibration-based fault detection for rolling bearing Multiscale entropy and its improvement …