New statistical machine learning methods applied to high dimensional sensory input data from chemistry

Machine learning is the concept where a computer can be trained to recognize data and predict future outcomes based on the trends that exist in the data. This method of analysis has not been used on engine data, specifically in-line oil. Oil is an engine’s lifeblood and a lot of data can be collected and engine health can be predicted based on these measurements. This project aims to deploy machine learning concepts in the area of engine failure prediction. A special sensor equipped with the machine learning algorithm will be able to report all vital signs of an engine in a matter of minutes.

Intern: 
Borislav Mavrin
Faculty Supervisor: 
Linglong Kong
Project Year: 
2017
Province: 
Alberta
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