Software Lifecycle Management Unsupervised Anomaly Detection

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dc.contributor.authorFriborg, Ludwig
dc.contributor.authorChristoffersson, Victor
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.date.accessioned2019-07-03T14:28:30Z
dc.date.available2019-07-03T14:28:30Z
dc.date.issued2017
dc.description.abstractThe purpose of this thesis is to evaluate if unsupervised anomaly detection, the task of nding anomalies in unlabelled data, can be used as a supportive tool for software life cycle management in nding errors which are tedious to detect manually. The goal is to apply the techniques of unsupervised machine learning on data-sets that are collected and analysed from a miniature-scaled research vehicle system that resembles the operation of a real automotive vehicles electrical architecture. Using a stacked autoencoder implemented with TensorFlow, the nal application is able to detect anomalies within the collected data-sets from the research vehicle. This proves the concept of utilising machine learning for error detection as a viable method. Finally concluding whether the techniques of unsupervised anomaly detection is applicable on a larger scale for real automotive vehicles.
dc.identifier.urihttps://hdl.handle.net/20.500.12380/250028
dc.language.isoeng
dc.setspec.uppsokTechnology
dc.subjectData- och informationsvetenskap
dc.subjectInformations- och kommunikationsteknik
dc.subjectComputer and Information Science
dc.subjectInformation & Communication Technology
dc.titleSoftware Lifecycle Management Unsupervised Anomaly Detection
dc.type.degreeExamensarbete på grundnivåsv
dc.type.uppsokM
local.programmeDatateknik 180 hp (högskoleingenjör)
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