The extraction of significant features is essential for efficient fault diagnosis and prognosis of
rolling element bearing. Data fusion is the predominant technology for extracting significant …

In recent years, deep learning technique has been used in mechanical intelligent fault
diagnosis and it has achieved much success. Among the deep learning models …

Rolling bearing fault diagnosis is an important and time sensitive task, to ensure the normal
operation of rotating machinery. This paper proposes a fault diagnosis for rolling bearings …

The entropy-based method has been demonstrated to be an effective approach to extract
the fault features by estimating the complexity of signals, but how to remove the strong …

The efficient diagnosis of bearing faults requires the extraction of informative features. This
paper presents a novel approach that combines Weighted Principal Component Analysis …

As the essential components of rotating machines, rolling bearings always operate in
variable working conditions and suffer from different failure modes. To address the issue of …

It is generally difficult to obtain a large number of labeled samples (ie, samples with known
fault types) of rolling bearings installed on large-scale mechanical equipment under current …

This paper presents a novel signal processing scheme by combining refined composite
hierarchical fuzzy entropy (RCHFE) and random forest (RF) for fault diagnosis of planetary …

Unsupervised cross-domain fault diagnosis research of rotating machinery has significant
implications. However, some issues remain to be solved. For example, convolutional neural …

The combination of nonlinear spectrum and convolutional neural network (CNN) is efficient
for fault diagnosis of nonlinear system. However, in traditional method, the nonlinear …