The monitoring of rotating machinery is an essential task in today's production processes.
Currently, several machine learning and deep learning-based modules have achieved …

Fault diagnosis is important for the maintenance of rotating machinery. The detection of
faults and fault patterns is a challenging part of machinery fault diagnosis. To tackle this …

Due to the growing interest for increasing productivity and cost reduction in industrial
environment, new techniques for monitoring rotating machinery are emerging. Artificial …

Although the effectiveness of machine learning (ML) for machine diagnosis has been widely
established, the interpretation of the diagnosis outcomes is still an open issue. Machine …

The analysis of vibration signals has been a very important technique for fault diagnosis and
health management of rotating machinery. Classic fault diagnosis methods are mainly …

The traditional intelligent diagnosis methods of rotating machinery generally require feature
extraction of the raw signals in advance. However, it is a very time-consuming and laborious …

Fault diagnosis of rotating machinery plays a significant role in the reliability and safety of
modern industrial systems. The traditional fault diagnosis methods usually need manually …

Aiming to promptly process the massive fault data and automatically provide accurate
diagnosis results, numerous studies have been conducted on intelligent fault diagnosis of …

Application of deep-learning techniques has been increasing, which redefines state-of-the-
art technology, especially in industrial applications such as fault diagnosis and classification …

Vibration analysis is a well-established technique for condition monitoring of rotating
machines as the vibration patterns differ depending on the fault or machine condition …