This article presents an intelligent fault diagnosis method for wind turbine (WT) gearbox by
using wavelet packet decomposition (WPD) and deep learning. Specifically, the vibration …

Traditional intelligent fault diagnosis techniques based on artificially selected features fail to
make the most of the raw data information, and are short of the capabilities of feature self …

This paper presents an integrated approach for the detection and classification of the faults
of rolling bearing in rotary machines. Permutation entropy (PE) is integrated with a flexible …

Feature extraction is one of the challenging problems in fault diagnosis, and it has a direct
bearing on the accuracy of fault diagnosis. Therefore, in this paper, a new method based on …

The working conditions of rotating machinery are more compound and the operating
conditions are more severe in the context of intelligent manufacturing, leading to more …

Fault diagnosis of rolling bearings depends on the construction of an effective index and a
reasonable identification method of fault features. In this paper, an effective method to …

Empirical wavelet transform (EWT) is an adaptive tool for vibration signals processing and
has been adopted for fault diagnosis of rolling bearings, while still suffers two weaknesses in …

The self-organizing fuzzy (SOF) logic classifier is an efficient and non-parametric classifier.
Its classification process is divided into an offline training stage, an online training stage, and …

As one of the most critical components in rotating machinery, it is essential to determine the
health of rolling bearings on time. The effective feature extraction method is considered …

Bearing failures often result from compound faults, where the characteristics of these
compound faults span across multiple domains. To tackle the challenge of extracting …