Machine Learning on the Edge

dc.contributor.authorLÖVGREN, JOHAN
dc.contributor.authorOlsson, Anton
dc.contributor.departmentChalmers tekniska högskola / Institutionen för data- och informationsteknik (Chalmers)sv
dc.contributor.departmentChalmers University of Technology / Department of Computer Science and Engineering (Chalmers)en
dc.description.abstractMachine learning is a tool for data analysis which can construct a model of a system without necessarily requiring deeper insight into the system. This model can then be used for analysis of the system. This type of analysis is of great interest for use with so called predictive maintenance; to be able to discover an abnormality in the behaviour in the system which can lead to the system breaking, before it actually happens. A platform for machine learning was provided by Ekkono and used to predict the state of a small DC motor. The prediction used data from various sensors to gain information about the current state of the system. This data was used to predict how the state would change a short time in the future. Fault detection was not within the scope of this project, we only concern ourselves with predicting how the values read from the sensors would change. In order to achieve this, a system which could be used for data collection was constructed. The system consisted of an Arduino Uno which collected data about the DC motor using various sensors. This data was analysed using a Raspberry Pi. The prediction was also done on the Raspberry. The accuracy of the predictions was then analysed.
dc.subjectInformations- och kommunikationsteknik
dc.subjectData- och informationsvetenskap
dc.subjectInformation & Communication Technology
dc.subjectComputer and Information Science
dc.titleMachine Learning on the Edge
dc.type.degreeExamensarbete på grundnivåsv
local.programmeDatateknik 180 hp (högskoleingenjör)
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