Rotating machines have been widely used in industrial engineering. The fault diagnosis of
rotating machines plays a vital important role to reduce the catastrophic failures and heavy …

There has been an increasing trend towards the use of complexity analysis in quantifying
neural activity measured by electroencephalography (EEG) signals. On top of revealing …

To timely detect the incipient failure of rolling bearing and find out the accurate fault location,
a novel rolling bearing fault diagnosis method is proposed based on the composite …

Intelligent fault diagnosis of rotating machinery is essentially a pattern recognition problem.
Meanwhile, effective feature extraction from the raw vibration signal is an important …

Most intelligent fault diagnosis methods of rotating machinery generally consider that normal
samples and fault samples as equally important for pattern recognition training. It ignores …

Slope entropy (SloEn) is an effective nonlinear dynamic method to represent the complexity
of time series, which has been extensively applied to various mechanical signal processing …

To promote the effect of variational mode decomposition (VMD) and further enhance the
recognition performances of bearing fault signals, genetic algorithm (GA) is applied to …

In this work, genetic algorithm (GA), kernel based mutual information (KEMI) fitness function
and variational mode decomposition (VMD) based strategy is proposed for the purpose of …

Timely and effective fault detection is essential to ensure the safe and reliable operation of
wind turbines. However, due to the complex kinematic mechanisms and harsh working …

A new bearing vibration feature extraction method based on multiscale permutation entropy
(MPE) and improved support vector machine based binary tree (ISVM-BT) is put forward in …