Intelligent fault diagnosis (IFD) refers to applications of machine learning theories to
machine fault diagnosis. This is a promising way to release the contribution from human …

Abstract Nowadays, Deep Learning is the most attractive research trend in the area of
Machine Learning. With the ability of learning features from raw data by deep architectures …

Intelligent fault diagnosis techniques have replaced time-consuming and unreliable human
analysis, increasing the efficiency of fault diagnosis. Deep learning models can improve the …

Sensitive feature extraction from the raw vibration signal is still a great challenge for
intelligent fault diagnosis of rolling bearing. Current fault classification framework generally …

The entropy-based method has been demonstrated to be an effective approach to extract
the fault features by estimating the complexity of signals, but how to remove the strong …

Accurate fault information is critical for optimal scheduling of production activities, improving
system reliability, and reducing operation and maintenance costs. In recent years, many fault …

Deep learning can play a pivotal role in early fault detection in squirrel cage induction
motors (SCIMs) and achieving Industry 4.0. SCIM finds application in industries like mining …

Intelligent fault diagnosis of bearings is an essential issue in the field of health management
and the prediction of rotating machinery systems. The traditional bearing intelligent …

Previous bearing fault diagnosis models show either low accuracy or long iterations, which
are not suitable for real-time production quality control scenarios lacking computing …

Machine learning methods are widely used for rolling bearing fault diagnosis. Most of them
are based on a basic assumption that training and testing data are adequate and follow the …