Software Lifecycle Management Unsupervised Anomaly Detection
dc.contributor.author | Friborg, Ludwig | |
dc.contributor.author | Christoffersson, Victor | |
dc.contributor.department | Chalmers tekniska högskola / Institutionen för data- och informationsteknik (Chalmers) | sv |
dc.contributor.department | Chalmers University of Technology / Department of Computer Science and Engineering (Chalmers) | en |
dc.date.accessioned | 2019-07-03T14:28:30Z | |
dc.date.available | 2019-07-03T14:28:30Z | |
dc.date.issued | 2017 | |
dc.description.abstract | The 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.uri | https://hdl.handle.net/20.500.12380/250028 | |
dc.language.iso | eng | |
dc.setspec.uppsok | Technology | |
dc.subject | Data- och informationsvetenskap | |
dc.subject | Informations- och kommunikationsteknik | |
dc.subject | Computer and Information Science | |
dc.subject | Information & Communication Technology | |
dc.title | Software Lifecycle Management Unsupervised Anomaly Detection | |
dc.type.degree | Examensarbete på grundnivå | sv |
dc.type.uppsok | M | |
local.programme | Datateknik 180 hp (högskoleingenjör) |
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