Prognostics and health management (PHM) plays a constructive role in the equipment's
entire life health service. It has long benefited from intensive research into physics modeling …

With the advent of 5G commercialization, the need for more reliable, faster, and intelligent
telecommunication systems is envisaged for the next generation beyond 5G (B5G) radio …

Deep learning (DL) has present great diagnostic results in fault diagnosis field. However, the
poor interpretability and noise robustness of DL-based methods are still the main factors …

This article proposes a new end-to-end deep learning model for fault diagnosis using three-
axis vibration signals measured from facilities. The three-axis vibration signals measured in …

With increasing customer demand, industry 4.0 gained a lot of interest, which is based on
smart factories. In smart factories, robotic components are vulnerable to failure due to …

Robustness of neural network models is important in fault diagnosis (FD) because
uncertainty in operating conditions varies the power spectral densities of vibration data; …

In the era of “big data”, artificial intelligence (AI) has emerged as an important approach in
the field of industrial intelligent diagnosis, owing to its powerful data mining and learning …

Cancer risk algorithms are developed in ever-increasing numbers to support clinical
decisions. However, their uptake in UK primary care remains low and there is little evidence …

Machine learning (ML) revolutionized traditional machine fault detection and identification
(FDI), as complex-structured models with well-designed unsupervised learning strategies …

Due to their performance in this field, Long-Short-Term Memory Neural Network (LSTM)
approaches are often used to predict the remaining useful life (RUL). However, their …