Data analysis has been one of the core activities in scientific research, but limited by the
availability of analysis methods in the past, data analysis was often relegated to data …

Signal processing methods are essential in scientific research, and time-frequency analysis
techniques such as Fourier Transform constitute an important progress in data analysis, but …

During the late 1990s, Huang introduced the algorithm called Empirical Mode
Decomposition, which is widely used today to recursively decompose a signal into different …

Empirical mode decomposition (EMD) is a way of decomposing complex signals into a sum
of “mono-components”, ie intrinsic mode functions (IMFs), each of which can be considered …

Some recent methods, like the empirical mode decomposition (EMD), propose to
decompose a signal accordingly to its contained information. Even though its adaptability …

In this paper an algorithm based on the ensemble empirical mode decomposition (EEMD) is
presented. The key idea on the EEMD relies on averaging the modes obtained by EMD …

The vibration signals of faulty rotating machinery are typically nonstationary, nonlinear, and
mixed with abundant compounded background noise. To extract the potential excitations …

The phenomenon of mode-mixing caused by intermittence signals is an annoying problem
in Empirical Mode Decomposition (EMD) method. The noise assisted method of Ensemble …

Adaptive extraction of concerned components (CC) from mixed frequency components
remains to be a challenging topic in various research domains. Most existing adaptive mode …

A new Ensemble Empirical Mode Decomposition (EEMD) is presented. This new approach
consists of sifting an ensemble of white noise-added signal (data) and treats the mean as …