Abstract Machine learning (ML) and deep learning (DL) have enabled algorithms to
autonomously acquire knowledge from data, facilitating predictive and decision-making …

Floods are a devastating natural calamity that may seriously harm both infrastructure and
people. Accurate flood forecasts and control are essential to lessen these effects and …

Fault diagnosis of bearings in rotating machinery is a critical task. Vibration signals are a
valuable source of information, but they can be complex and noisy. A transformer model can …

Fault detection and diagnosis play a crucial role in ensuring the reliability and safety of
modern industrial systems. For safety and cost considerations, critical equipment and …

The installed wind power capacity is growing worldwide. Remote condition monitoring of
wind turbines is employed to achieve higher up-times and lower maintenance costs …

In this paper, we propose the gradient-oriented prioritization meta-learning (GOPML)
algorithm, a new approach for few-shot fault diagnosis in industrial systems. The GOPML …

The furniture manufacturing sector faces significant challenges in machining composite
materials, where quality issues such as delamination can lead to substandard products. This …

The monitoring of the health of Rolling Element Bearings (REBs) in the rolling mill process
was recently automated through signal processing and machine learning technologies …

Industries rely on the early and efficient detection of bearing faults to enhance the service life
of three-phase induction motors. Various bearing health condition monitoring techniques …

The application of edge computing combined with the Internet of Things (edge-IoT) has
been rapidly developed. It is of great significance to develop a lightweight network for …