Recently, to ensure the reliability and safety of high-speed trains, detection and diagnosis of
faults (FDD) in traction systems have become an active issue in the transportation area over …

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 …

In recent years, data-driven methods have been widely used in rolling bearing fault
diagnosis with great success, which mainly relies on the same data distribution and massive …

Various deep learning methodologies have recently been developed for machine condition
monitoring recently, and they have achieved impressive success in bearing fault …

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 article develops new deep learning methods, namely, deep residual shrinkage
networks, to improve the feature learning ability from highly noised vibration signals and …

The complex and changeable working environment of wind turbine often challenges the
condition monitoring and fault detection. In this paper, a new method is proposed for fault …

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 …