Rolling-element bearing is one of the main parts of rotating equipment. In order to avoid the
mechanical equipment damage caused by the sudden failure of rolling-element bearings, it …

This paper proposed a novel fault recognition method for rotating machinery on the basis of
multi-sensor data fusion and bottleneck layer optimized convolutional neural network (MB …

Considering various health conditions under varying operational conditions, the mining
sensitive feature from the measured signals is still a great challenge for intelligent fault …

Efficient and accurate planetary gearbox fault diagnosis is the key to enhance the reliability
and security of wind turbines. Therefore, an intelligent and integrated approach based on …

Denoising autoencoders can automatically learn in-depth features from complex data and
extract concise expressions, which are used in fault diagnosis. However, they still have …

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 …

The rolling bearing is a key element of rotating machine and its fault diagnosis is a research
focus. When a single fault of a rolling bearing fails to be addressed in time, it will cause …

Effective feature extraction is crucial for accurate fault diagnosis of rolling bearings. A novel
feature extraction method called hierarchical dispersion entropy (HDE) based on …

The order tracking method based on time-frequency representation is regarded as an
effective tool for fault detection of bearings with varying rotating speeds. In the traditional …

In the health monitoring of rotating machinery, there often coexists multiple fault sources.
Thus a multi-source compound fault signal will be excited and collected by sensors …