Software Lifecycle Management Unsupervised Anomaly Detection

Examensarbete på grundnivå

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Type: Examensarbete på grundnivå
Title: Software Lifecycle Management Unsupervised Anomaly Detection
Authors: Friborg, Ludwig
Christoffersson, Victor
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.
Keywords: Data- och informationsvetenskap;Informations- och kommunikationsteknik;Computer and Information Science;Information & Communication Technology
Issue Date: 2017
Publisher: Chalmers tekniska högskola / Institutionen för data- och informationsteknik (Chalmers)
Chalmers University of Technology / Department of Computer Science and Engineering (Chalmers)
Collection:Examensarbeten på grundnivå // Basic Level Theses

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