PORTALE DELLA DIDATTICA

Ricerca CERCA
  KEYWORD

Area Architecture

Automated Internal Inspection of Wind Turbine Blades Using Computer Vision and Machine Learning Techniques

estero Thesis abroad


keywords COMPUTER VISION, DAMAGE DETECTION, INSPECTION, MACHINE LEARNING, WIND TURBINE, WIND TURBINE BLADES

Reference persons MARCO CIVERA, CECILIA SURACE

External reference persons Prof Alessandro Sabato, University of Massachusetts Lowell

Thesis type RESEARCH / EXPERIMENTAL

Description Computer Vision technologies capable of recreating point clouds are becoming more important in the field of structural health monitoring. The possibility of using mobile systems such as drones or robots capable of reaching points otherwise unreachable has increased the scientific community's interest in these systems.
The aim of the thesis is the definition of a methodology for the creation of a system to inspect the inside of wind turbine blades, recreate reconstructions of the internal surface of the blades, and identify changes in the structure over time.
Specifically, starting from data acquisitions carried out with drones with RGB and infrared cameras and LiDAR scanners, the candidate will have to define a procedure to reconstruct a point cloud of the geometry of the inside of wind turbine blades and use Machine Learning techniques to automate the recognition of defects such as splits, delaminations and cleavages.
The work involves an evaluation of the system in the laboratory (with predefined and known boundary conditions) and the possibility of testing the developed system in situ on wind turbines with a length of up to 75 metres.

Required skills MATLAB, possibly prior knowledge of Machine Learning algorithms

Notes Average grade required: >= 27/30 and knowledge of the English language at least B2/C1 level


Deadline 23/11/2024      PROPONI LA TUA CANDIDATURA