The traditional deep learning-based bearing fault diagnosis approaches assume that the
training and test data follow the same distribution. This assumption, however, is not always …

The current development in deep learning is witnessing an exponential transition into
automation applications. This automation transition can provide a promising framework for …

Data scarcity is a major challenge when training deep learning (DL) models. DL demands a
large amount of data to achieve exceptional performance. Unfortunately, many applications …

Various deep learning methodologies have recently been developed for machine condition
monitoring recently, and they have achieved impressive success in bearing fault …

Most of the existing research on unsupervised cross-domain intelligent fault diagnosis is
based on single-source domain adaptation, which fails to simultaneously utilize various …

Fault diagnosis of rolling bearings has attracted extensive attention in industrial fields, which
plays a vital role in guaranteeing the reliability, safety, and economical efficiency of …

Accurate state of charge (SOC) constitutes the basis for reliable operations of lithium-ion
batteries. The deep learning technique, a game changer in many fields, has recently …

Accurate remaining useful life (RUL) prediction for rolling bearings encounters many
challenges such as complex degradation processes, varying working conditions, and …

As an emerging technology in the era of Industry 4.0, digital twin is gaining unprecedented
attention because of its promise to further optimize process design, quality control, health …

Most data-driven methods for fault diagnostics rely on the assumption of independently and
identically distributed data of training and testing. However, domain shift between the …