A time–frequency analysis method based on ensemble local mean decomposition (ELMD)
and fast kurtogram (FK) is proposed for rotating machinery fault diagnosis. Local mean …

Local mean decomposition (LMD) is a novel self-adaptive time–frequency analysis method,
which is particularly suitable for the processing of multi-component amplitude-modulated …

Health diagnosis of the rotating machinery can identify potential failure at its early stage and
reduce severe machine damage and costly machine downtime. In recent years, the adaptive …

Rotating machinery is widely used in the industry. They are vulnerable to many kinds of
damages especially for those working under tough and time-varying operation conditions …

Fault feature extraction is crucial to detect failures as earlier as possible in fault diagnosis of
rotating machinery. Due to the influence of environment noise and interference, the signal to …

In this paper, an empirical mode decomposition (EMD) based approach for rotating machine
fault diagnosis is investigated. EMD is a new time–frequency analyzing method for nonlinear …

In order to extract fault features of large-scale power equipment from strong background
noise, a hybrid fault diagnosis method based on the second generation wavelet de-noising …

Targeting the shock characteristics of the vibration signal of a rotor system with local rub-
impact fault, a local rub-impact fault diagnosis method of rotor system based on ELMD …

Empirical mode decomposition (EMD) is a self-adaptive analysis method for nonlinear and
non-stationary signals. It may decompose a complicated signal into a collection of intrinsic …

After analyzing the shortcomings of current feature extraction and fault diagnosis
technologies, a new approach based on wavelet packet decomposition (WPD) and …