Slope entropy (SloEn) is an effective complexity analysis measure of signals that has been
applied to many areas in recent years. Whereas SloEn can only reflect the complexity …

Feature extraction is a key step in intelligent bearing fault diagnosis. However, bearing
vibration signals are usually nonlinear, nonstationary signal with strong noises. Extracting …

Bearing is the key part of mechanical equipment, which can support the rotating machinery
running. It is crucial to diagnose bearing faults in time to ensure mechanical equipment …

The Katz fractal dimension (KFD) is an effective nonlinear dynamic metric that characterizes
the complexity of time series by calculating the distance between two consecutive points and …

In order to further improve the accuracy of fault identification of rolling bearings, a fault
diagnosis method based on the modified particle swarm optimization (MPSO) algorithm …

In existing research on rotating machinery diagnosis using graph neural networks (GNNs),
most methods are based on vibration analysis under contact sensor monitoring. However …

Railway point machines are the key equipment that controls the train route and affects the
safety of train operation. Complex and harsh working environments lead to frequent failures …

Low-voltage circuit breaker (LVCB) fault diagnosis based on artificial intelligence (AI)
algorithm has always been a research hotspot and got some recent advances. However, AI …

Rolling element bearings are a critical component of rotating machines, and the presence of
defects in the bearings may eventually lead to machine failure. Hence, early identification of …

Monitoring depth of anaesthesia (DoA) from electroencephalograph (EEG) signals is an
ongoing challenge for anaesthesiologists. In this study, we propose an intelligence model …