Obtaining the sensitive feature vectors from the vibration signal is crucial to indicate the
bearing's actual condition. Most often, weak feature vectors are the consequence of heavy …

Returning straw-like agricultural waste to the field by converting it into hydrochar through
hydrothermal carbonization (HTC) is an important way to realize resource utilization of …

Assessment of the dynamical complexity of signals or systems is very crucial in medical
diagnostics, fault analysis of mechanical systems, astrophysics and many more. Although …

K-Means clustering algorithm is a typical unsupervised learning method, and it has been
widely used in the field of fault diagnosis. However, the traditional serial K-Means clustering …

In the signal analysis context, the entropy concept can characterize signal properties for
detecting anomalies or non-representative behaviors in fiscal systems. In motor fault …

Machine learning algorithms play an important role in the detection of toxic, flammable and
explosive gases, and they are extremely important for the study of mixed gas classification …

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 effectively predict the rolling bearing fault under different working conditions, a rolling
bearing fault prediction method based on quantum particle swarm optimization (QPSO) …

The recurrent use of databases with categorical variables in different applications demands
new alternatives to identify relevant patterns. Classification is an interesting approach for the …