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 …

Health condition monitoring of rotating machinery is a crucial task to guarantee reliability in
industrial processes. In particular, bearings are mechanical components used in most …

Being an effective methodology to adaptatively decompose a multi-component signal into a
series of amplitude-modulated-frequency-modulated (AMFM) sub-signals with limited …

In recent years, deep learning has become an emerging research orientation in the field of
intelligent monitoring and fault diagnosis for industry equipment. Generally, the success of …

Intelligent fault diagnosis techniques play an important role in improving the abilities of
automated monitoring, inference, and decision making for the repair and maintenance of …

In recent years, an increasing popularity of deep learning model for intelligent condition
monitoring and diagnosis as well as prognostics used for mechanical systems and …

The operation conditions of the rotating machinery are always complex and variable, which
makes it difficult to automatically and effectively capture the useful fault features from the …

Automatic and accurate identification of rolling bearings fault categories, especially for the
fault severities and fault orientations, is still a major challenge in rotating machinery fault …

Extracting weak fault features under noise interference is crucial for the fault diagnosis of
rolling bearings at an early stage. In this paper, a new method based on improved ensemble …

Bearing fault diagnosis is of significance to enhance the reliability and security of electric
locomotive. In this paper, a novel convolutional deep belief network (CDBN) is proposed for …