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- W2934209481 abstract "Wind energy plays a major role in the ongoing energy transition. To accelerate the adoption of wind energy and thereby the energy transition, the Levelized Cost of Energy (LCOE) has to be minimized. Apart from increasing turbine performance, reducing turbine down-time can contribute to lowering the LCOE.Down-time is defined as time during which a windturbine is not able to generate energy due to being put in a parked configuration. This occurs when a windturbine has failed or an alarm has went off and the turbine is awaiting inspection. Gearbox failures are identified as main drivers for down-time.Computer vision is an emerging inspection technique in industry that is able to continuously monitor various processes. It is therefore interesting to investigate the integration of computer vision with windturbine gearbox with the aim of reducing down-time. In this research, quality checks and online condition monitoring of a windturbine gearbox are established as fields that are suitable for computer vision integration. This is narrowed down to gear alignment checks and online pitting detection, respectively. The main contribution of this thesis is two-fold. First, a method based on computer vision is proposed that is able to check and measure gear alignment using a simple 2D camera integrated in the gearbox and thus without 3D range imaging. Furthermore, the method reconstructs a virtual gear tooth and provides a direct comparison with gear alignment simulations. This provides a time-efficient and objective measure, enabling gear alignment checks to be included in quality checks of serial produced gearboxes. Second, a novel method for online pitting detection is proposed based on Deep-Learning. The method is tested on pitting damage generated with a test-rig and it is shown that Deep-Learning based pitting detection has great potential. Moreover, computer vision based condition monitoring would be insensitive to load and speed variation which is the deficiency of current state of the art condition monitoring techniques." @default.
- W2934209481 created "2019-04-11" @default.
- W2934209481 creator A5047465786 @default.
- W2934209481 date "2019-01-01" @default.
- W2934209481 modified "2023-09-27" @default.
- W2934209481 title "Computer Vision for Gear Alignment Check and Condition Monitoring of Wind Turbine Gearboxes" @default.
- W2934209481 hasPublicationYear "2019" @default.
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