Rotating machinery often works under time-varying speeds, and nonstationary conditions
and harsh environments make its key parts, such as rolling bearings and gears, prone to …

Over the last 20 years, particularly in last 10 years, great progress has been made in the
theory and applications of wavelets and many publications have been seen in the field of …

With significant advantages in feature learning, the deep learning-based compound fault
(CF) diagnosis method has brought many successful applications for industrial equipment; …

Convolutional neural network (CNN), with the ability of feature learning and nonlinear
map**, has demonstrated its effectiveness in prognostics and health management (PHM) …

This paper considers the analysis of multicomponent signals, defined as superpositions of
real or complex modulated waves. It introduces two new post-transformations for the short …

Typical microseismic data recorded by surface arrays are characterized by low signal-to-
noise ratios (S/Ns) and highly nonstationary noise that make it difficult to detect small events …

Synchrosqueezing transform (SST) is an effective post-processing time-frequency analysis
(TFA) method in mechanical signal processing. It improves the concentration of the time …

The synchrosqueezing transform, a kind of reassignment method, aims to sharpen the time-
frequency representation and to separate the components of a multicomponent non …

With the advancement of artificial intelligence and the accumulation of industrial big data,
intelligent diagnosis methods based on deep learning have become the mainstream for …

The authors introduce an iterative algorithm, called matching demodulation transform (MDT),
to generate a time-frequency (TF) representation with satisfactory energy concentration. As …