The application of deep learning-based rolling bearing fault diagnosis methods in high
reliability scenarios is limited due to low transparency. In addition, the scaling up of the deep …

Analyzing vibration data using deep neural networks is an effective way to detect damages
in rotating machinery at an early stage. However, the black-box approach of these methods …

Microsystems are widely used in 5G, the Internet of Things, smart electronic devices and
other fields, and signal integrity (SI) determines their performance. Establishing accurate …

The detection of anomalies in multivariate time-series data is becoming increasingly
important in the automated and continuous monitoring of complex systems and devices due …

Wind energy is a promising renewable source, necessitating effective monitoring of wind
turbine (WT) conditions for reliable and cost-effective energy production, amidst …

For remanufacturing to be more economically attractive, there is a need to develop
automatic disassembly and automated visual detection methods. Screw removal is a …

Industrial automation systems are excessively used in advanced manufacturing
environments. These systems are always prone to failure which not only disturbs smooth …

Abstract Industry 4.0 denotes smart manufacturing, where rotating machines predominantly
serve as the fundamental components in production sectors. The primary duty of …

This work is focused on the realization of a low-complexity MobileNet neural network able to
classify different bearing failure vibration signals. The network was designed to be deployed …

Multi-sensor technology has been widely applied in the condition monitoring of rail transit. In
practice, the data of some channels in the high channel counts are often abnormal or lost …