The traditional rolling bearing fault diagnosis methods are difficult to achieve effective cross-
domain fault diagnosis. Therefore, a novel deep convolution multi-adversarial domain …

Fault feature extraction and broadband noise elimination are the keys to weak bearing fault
diagnosis. Morphological filtering is a typical fault feature extraction method. However, the …

Fault detection and diagnosis of its severity for machine health monitoring can be stated as a
nested classification problem. For a faulty bearing, each fault location whether belonging to …

The relevance vector machine (RVM) is considered a robust machine learning method and
its superior performance has been confirmed through many successful engineering …

This study employs a supervised learning method to predict the tunnel boring machine
(TBM) penetration rate (PR) with high accuracy. To this end, the extreme gradient boosting …

The vibration signal induced by bearing local fault has strong nonstationary and nonlinear
property, which indicates that the conventional methods are difficult to recognize bearing …

Deep learning-based fault diagnosis usually requires a rich supply of data, but fault samples
are scarce in practice, posing a considerable challenge for existing diagnosis approaches to …

In order to detect the incipient fault of rolling bearings and to effectively identify fault
characteristics, based on amplitude-aware permutation entropy (AAPE), an enhanced …

This paper proposes a novel fault diagnosis method for rolling bearing based on
hierarchical refined composite multiscale fluctuation-based dispersion entropy (HRCMFDE) …

To avoid production interruptions and equipment damage caused by rolling bearing failure,
this study presents a novel diagnosis scheme applicable to both condition monitoring and …