Among the diverse challenges in machining processes, chatter has a significant detrimental
effect on surface quality and tool life, and it is a major limitation factor in achieving higher …

Safety and reliability are absolutely vital for sophisticated Railway Point Machines (RPMs).
Hence, various kinds of sensors and transducers are deployed on RPMs as much as …

With significant advantages in feature learning, the deep learning-based compound fault
(CF) diagnosis method has brought many successful applications for industrial equipment; …

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 …

Due to the complex and rugged working environment of real machinery equipment, the
resulting fault information is easily submerged by severe noise interference. Additionally …

Intelligent fault diagnosis techniques play an important role in improving the abilities of
automated monitoring, inference, and decision making for the repair and maintenance of …

Abstract Lempel–Ziv complexity (LZC), as a nonlinear feature in information science, has
shown great promise in detecting correlations and capturing dynamic changes in single …

Most intelligent fault diagnosis methods of rotating machinery generally consider that normal
samples and fault samples as equally important for pattern recognition training. It ignores …

An increasing number of studies across many research fields from biomedical engineering
to finance are employing measures of entropy to quantify the regularity, variability or …

Deep learning (DL) has present great diagnostic results in fault diagnosis field. However, the
poor interpretability and noise robustness of DL-based methods are still the main factors …