With the continuous progress of modern science and technology, machinery is developing towards increasingly high speed and complexity. The failure of gears, rolling bearings, steam turbine rotors, and other important rotating mechanical equipment can cause financial losses and catastrophic accidents. Detection and feature extraction of vibration signals of rotating machinery are two crucial steps to ensure the safe operation of equipment.

In recent literature, vibration signal detection methods based on stochastic resonance and feature extraction methods based on entropy show great advantages in signal detection and feature extraction, respectively. Extracting the entropy features of these enhanced vibration signals can produce more information about the working state of the structure, which is of great practical significance for the safe operation of complex mechanical systems. However, the accuracy of entropy features is easily affected by noise. Therefore, improving existing entropy features and signal detection methods is still a topic of great interest in the field of vibration signal analysis.

This Special Issue welcomes both original research and review articles on entropy feature extraction and state detection of vibration signals in various fields. We invite scientists and investigators to contribute original research and review articles to solve the main issues facing the field.

Potential topics include but are not limited to the following:

• Feature extraction of ship signals based on entropy
• Vibration signal detection based on stochastic resonance
• Vibration signal detection based on the Duffing chaotic oscillator
• State recognition of underwater acoustic signals
• Fault diagnosis of rolling bearing signals based on entropy and mode decomposition
• Feature extraction of rolling bearing signals based on entropy and the Duffing chaotic oscillator
• Vibration signal feature extraction based on optimization algorithms and mode decomposition
• Vibration signal classification with deep features