With the rapid development of industry, fault diagnosis plays a more and more important role
in maintaining the health of equipment and ensuring the safe operation of equipment. Due to …

In modern industry, the quality of maintenance directly influences equipment's operational
uptime and efficiency. Hence, based on monitoring the condition of the machinery, predictive …

Monitoring structural damage is extremely important for sustaining and preserving the
service life of civil structures. While successful monitoring provides resolute and staunch …

Intelligent fault diagnosis (IFD) refers to applications of machine learning theories to
machine fault diagnosis. This is a promising way to release the contribution from human …

Abstract Machine learning (ML) pervades an increasing number of academic disciplines and
industries. Its impact is profound, and several fields have been fundamentally altered by it …

This article develops new deep learning methods, namely, deep residual shrinkage
networks, to improve the feature learning ability from highly noised vibration signals and …

During the last decade, Convolutional Neural Networks (CNNs) have become the de facto
standard for various Computer Vision and Machine Learning operations. CNNs are feed …

The complex and changeable working environment of wind turbine often challenges the
condition monitoring and fault detection. In this paper, a new method is proposed for fault …

Deep learning applications have been thriving over the last decade in many different
domains, including computer vision and natural language understanding. The drivers for the …

Crop yield prediction is extremely challenging due to its dependence on multiple factors
such as crop genotype, environmental factors, management practices, and their interactions …