As a crucial and widely used component in industrial fields with great complexity, the health
condition of rotating machinery is directly related to production efficiency and safety …

Due to the non-stationary and nonlinear characteristics of rotating machinery fault signals, it
is difficult to identify different fault conditions using only traditional time-frequency domain …

Identifying fault of rotating machinery under different load conditions with high accuracy is a
remaining challenge for vibration signal based fault diagnosis. Aiming at this challenge, this …

In recent years, the variational mode decomposition (VMD) method has been introduced for
rotating machinery fault diagnosis. However, the results largely depend on its parameters …

As crucial equipment during industrial manufacture, the health status of rotating machinery
affects the production efficiency and device safety. Hence, it is of great significance to …

Owing to the character of diversity and complexity, the compound fault diagnosis of rotating
machinery under non-stationary operation has turned into a challenging task. In this paper, a …

Fault diagnosis methods based on machine learning have made great progress for rotating
machinery. The main steps of the machine learning process involve feature extraction …

Feature extraction and class discrimination are two key problems for fault diagnosis of
rotating machinery. Firstly, multi-scale higher order singular spectrum analysis (MS-HO …

Intelligent fault diagnosis of rotating machinery is vital for industries to improve fault
prediction performance and reduce the maintenance cost. The new fault diagnostic …

Multivariate variational mode decomposition (MVMD) is a novel extension of variational
mode decomposition (VMD) for multi-channel data sets. It decomposes multi-component …