Aiming at the application of deep learning in fault diagnosis, mechanical rotating equipment
components are prone to failure under complex working environment, and the industrial big …

Deep learning methods based on vibration signals of rotating machinery have been
continuously developed in fault diagnosis. However, there are still three challenges in …

Rotating machinery fault diagnosis is vital to enhance the reliability and safety of modern
equipment. Recently, deep learning (DL) models have achieved breakthrough …

Deep learning is characterized by strong self-learning and fault classification ability without
manually feature extraction stage of traditional algorithms. Deep belief network (DBN) is one …

As a breakthrough in the field of machine fault diagnosis, deep learning has great potential
to extract more abstract and discriminative features automatically without much prior …

As well-known, deep learning models have achieved great success in the field of intelligent
fault diagnosis. However, once the working condition changed, the diagnostic accuracy of …

In real industrial scenarios, the working conditions of mechanical equipment are always
highly variable and the amount of data that can be collected is limited, which renders a …

Currently, few samples and the inevitable noise poses a severe test on deep learning
methods. To solve the above problems, a novel fault diagnosis network based on a refined …

Recent advances in convolutional neural networks (CNN) have boosted the research on
reliability monitoring of rotating machinery. In actual industry production, the mechanical …

Generally, the health conditions of rotating machinery are complicated and changeable.
Meanwhile, its fault labeled information is mostly unknown. Therefore, it is man-sized to …