Thursday, 19th of May 2022, 12:00 – 1:00

Machine learning approaches for cylinder wear analysis

Venue: 
SR1

Lecturer:
Adéla Moravová - researcher at IGS

Abstract: 

Large engines are an vital part of many fields in industry, used, for example, in maritime transport and in power generation, as backups or as a global solution of the energy distribution. Therefore, the need to extend the engine's life span and optimize its maintenance is severe, as the energy and the economic costs are to be minimized.

This master thesis investigates specific part of the large engines; its cylinders. During the operation, the cylinder accumulates wear, which leads to suboptimal distribution of lubricant, which may grow  into a severe damage to the engine. RGB images and corresponding depth maps of the surface of the cylinders are analyzed in both un- and supervised approaches with various techniques. Lastly, a neural network predicts a depth map on a microscopic scale from a single surface image.

  

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