With the rapid development of industry, fault diagnosis plays a more and more important role
in maintaining the health of equipment and ensuring the safe operation of equipment. Due to …

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 …

Unsupervised cross-domain fault diagnosis of bearings has practical significance; however,
the existing studies still face some problems. For example, transfer diagnosis scenarios are …

Sensor techniques and emerging CNN models have greatly facilitated the development of
collaborative fault diagnosis. Existing CNN models apply different fusion schemes to …

Rolling bearing fault detection is critical for improving production efficiency and lowering
accident rates in complicated mechanical systems, as well as huge monitoring data, posing …

This paper presents a data-driven intelligent fault diagnosis approach for rotating machinery
(RM) based on a novel continuous wavelet transform-local binary convolutional neural …

Intelligent data-driven machinery fault diagnosis methods have been successfully and
popularly developed in the past years. While promising diagnostic performance has been …

With the rapid development of manufacturing industry, machine fault diagnosis has become
increasingly significant to ensure safe equipment operation and production. Consequently …

Safety and reliability are absolutely important for modern sophisticated systems and
technologies. Therefore, malfunction monitoring capabilities are instilled in the system for …

In this survey paper, we systematically summarize existing literature on bearing fault
diagnostics with deep learning (DL) algorithms. While conventional machine learning (ML) …