Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE credits
For this master thesis project we have been working towards modelling
the lifetime maintenance needs of a Volvo truck. Such a model
could accurately estimate problems a truck may encounter at any
given point in time. We were provided with records from workshop
visits going back over a period of 10 years. In this thesis we have
performed an exploratory data analysis involving both data mining
and machine learning techniques in order to extract the most useful
information from it. In order to separate different types of service
events from each other two different clustering techniques have been
used. Also, an operation distinction algorithm have been created to
separate maintenance operations from repair operations on the trucks.
In this thesis we have also pointed out issues in the data and given
suggestions for continues work towards building a model of a trucks
lifetime maintenance needs.