Supervised learning algorithms have shown limited use in the field of anomaly detection due
to the unpredictability and difficulty in acquiring abnormal samples. In recent years …

Currently, deep learning-based visual inspection has been highly successful with the help of
supervised learning methods. However, in real industrial scenarios, the scarcity of defect …

The rapid industrial growth in solar energy is gaining increasing interest in renewable power
from smart grids and plants. Anomaly detection in photovoltaic (PV) systems is a demanding …

Condition monitoring and inspection are core activities for assessing and evaluating the
health of critical infrastructure spanning from road networks to nuclear power stations. Defect …

Solar energy utilization in the industry has grown substantially, resulting in heightened
recognition of renewable energy sources from power plants and intelligent grid systems …

Accurate and efficient characterisation techniques are essential to ensure the optimal
performance and reliability of photovoltaic devices, especially given the large number of …

Anomaly detection methods are used to find abnormal states, instances or data points that
differ from a normal sample from the data domain space. Industrial processes are a domain …

Data analytics (DA) has gained significant attention within the context of Zero-Defect
Manufacturing (ZDM), as it holds the potential to enhance product quality through the …

During the production of energy using photovoltaic (PV) panels, solar cells may be affected
by different environmental aspects, which cause many defects in the solar cells. Such …

In the initial stage of the establishment of photovoltaic (PV) module production lines or the
upgrading of production processes, the available data for some defects are limited. The …