Manufacturing systems are becoming more sophisticated and expensive, particularly with
the development of the intelligent industry. The complexity of the architecture and concept of …

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 …

Considering about slow drift and complicated relationships among process variables
caused by corrosion, fatigue, and so on in complex chemical engineering processes, an …

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 …

Deep learning methods are essential for the application of data driven technologies on fault
diagnosis of rotating machinery. However, the generalization and performance of deep …

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 …

Focusing on the problems of complex structure and low feature extraction efficiency that
exist in some traditional neural network algorithms, an improved convolutional neural …

Very early bolt looseness monitoring has been a challenge in the field of structural health
monitoring. The authors have conducted a further study of the previous researches, with the …