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 …

This paper deals with the development of a novel deep learning framework to achieve highly
accurate rotating machinery fault diagnosis using residual wide-kernel deep convolutional …

Currently, it is a great challenge to effectively acquire more widespread equipment health
information for guaranteeing safe production and timely fault maintenance in the process of …

It is meaningful to automatically learn the valuable features from the raw vibration data and
provide accurate fault diagnosis results. In this paper, an enhancement deep feature fusion …

Early fault diagnosis of rotating machinery is crucial in the industry. The network parameters
of the traditional deep learning-based fault diagnosis method are optimized only by the …

In traditional intelligent fault diagnosis methods of rotating machinery, features are designed
manually by experts, which makes these methods less automatic. Deep auto-encoder (DA) …

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 …

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 …

Fault diagnosis of rotating machinery is vital to improve the security and reliability as well as
avoid serious accidents. For instance, robust fault features are crucial to achieve a high …

Intelligent machinery fault diagnosis system has been receiving increasing attention recently
due to the potential large benefits of maintenance cost reduction, enhanced operation safety …