The recent advance of digital twin (DT) has greatly facilitated the development of predictive
maintenance (PdM). DT for PdM enables accurate equipment status recognition and …

Prognostics and health management (PHM) is critical for enhancing equipment reliability
and reducing maintenance costs, and research on intelligent PHM has made significant …

Recent years have seen the rapid development and marvelous achievement of deep
learning-based fault diagnosis (FD) methods which assume that training data and testing …

With the advances in artificial intelligence, there is a growing expectation of more automatic
and intelligent prognostics and health management (PHM) systems for the real-time …

In consequence of their superior performance and durability, industrial robots have enjoyed
widespread adoption across a variety of industries. However, despite their sturdy build, they …

Dry friction clutches are prone to fault occurrences due to their continuous exposure to
thermal loading and high abrasive rate during power transmission. Fault occurrences in …

Scenarios data of renewable energy resources plays an essential role in the study of
mitigating the risk in the power system due to their intermittent nature. Existing researches …

In fault diagnosis, machine learning theories are gaining popularity as they proved to be an
efficient tool that not only reduces human effort but also identifies the health conditions of the …

As integral components within rotating machinery, bearings and gears pose a critical
challenge in fault diagnosis. Presently, data-driven fault diagnosis stands out as a viable …

Remaining useful life (RUL) estimation, a key component in predictive maintenance (PdM),
aims to reduce maintenance cycles in the prognostic health of mechanical equipment (s) …