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 …

Rotating machine faults are unavoidable; thus, early diagnosis is essential to avoid further
damage to the machine or other machine attached to it. Various signal analysis based …

Up to now, extensive literature on the usage of deep learning in manufacturing can be
found. Though, actual usage of deep learning in manufacturing sites is somehow restrained …

Fault diagnosis is an important part of the health management of many pieces of equipment.
It is an effective means to reduce equipment failure rate and shutdown loss. In engineering …

Background Convolutional autoencoder (CAE) is an unsupervised feature learning method
and shows excellent performance in multivariate fault diagnosis. However, CAE cannot …

The safe operation of diesel engines performs a vital function in industrial production and
life. Because diesel engines often work in harsh environmental conditions, they are prone to …

Gas path fault diagnosis plays a critical role in the security guarantee and maintenance of
aero-engines. In this paper, an approach based on a fusion neural network under multiple …

A steering actuator is the core component of an autonomous vehicle. Because of noise
interference, the extraction of useful fault features for an accurate fault diagnosis of steering …

In any industry, Equipment Asset Management (EAM) is at the core of the production
activities. With the rapid development of Industrial Internet technologies and platforms, the …

Fault diagnosis of rolling bearings is significant for mechanical equipment operation and
maintenance. Presently, the deep convolutional neural network (CNN) is increasingly used …