Early detection of blade defects is crucial for turbines in Internet of Things (IoT) as it can
prevent failures, minimize downtime, and enhance system reliability. Deep learning-based …

Blade health is directly related to the safety and efficiency of wind turbine (WT) operation. In
this article, a cloud-edge-end collaborative detection method for WT blade surface damage …

Wind turbine blades will suffer various surface damages due to their operating environment
and high-speed rotation. Accurate identification in the early stage of damage formation is …

Cracking of wind turbine blades is one of the main faults affecting the safety and efficiency of
wind turbines. Because the unit has multiple operating conditions, under different working …

Wind turbine blades are the core components responsible for efficient wind energy
conversion and ensuring stability. To address challenges in wind turbine blade damage …

In response to the shortcomings of existing image detection algorithms in the early damage
detection of wind turbine blades, such as insufficient applicability and unsatisfactory …

Casting defects in turbine blades can significantly reduce an aero-engine's service life and
cause secondary damage to the blades when exposed to harsh environments. Therefore …

In this paper, a deep neural network named Mask-MRNet is proposed to detect wind turbine
(WT) blade fault based on images taken by unmanned aerial vehicles. Two datasets of the …

Micro-defects on the surface of turbine blades can lead to aviation accidents, thus it is
important to thoroughly detect. To realize micro-defect detection, we propose a small object …

Recently, the machine-vision-based blades surface damage detection technique has
received great attention for its low cost, easy operation, and lack of a need for prior …