Anomaly Detection in Logged Sensor Data

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dc.contributor.authorFlorbäck, Johan
dc.contributor.departmentChalmers tekniska högskola / Institutionen för tillämpad mekaniksv
dc.contributor.departmentChalmers University of Technology / Department of Applied Mechanicsen
dc.date.accessioned2019-07-03T13:49:13Z
dc.date.available2019-07-03T13:49:13Z
dc.date.issued2015
dc.description.abstractAnomaly detection methods are used in a wide variety of fields to extract important information (e.g. credit card fraud, presence of tumours or sensor malfunctions). Current anomaly detection methods are data- or application specific; a general anomaly detection method would be a useful tool in many situations. In this thesis a general method based on statistics is developed and evaluated. The method includes well-known statistical tools as well as a novel algorithm (sensor profiling) which is introduced in this thesis. The general method makes use of correlations found in complex sensor systems, which consists of several sensor signals. The method is evaluated using real sensor data provided by Volvo Car Corporation. The sensor profiling can be used to find clusters of data with similar probability distributions. It is used to automatically determine the sensor performance across different external conditions. Evaluating the anomaly detection method on a data set with known anomalies in one sensor signal results in 94 % of anomalies detected at 6 % false detection rate. Evaluating the method on additional sensor signals was not done. The sensor profiling revealed conditions where the sensor signal behaves qualitatively and quantitatively different. It is able to do this in data where other commonly used methods, such as regression analysis, fail to extract any information. Sensor profiling may have additional applications beyond anomaly detection as it is able to extract information when other methods can not. To conclude, this thesis presents a seemingly natural method and tool chain to automatically detect anomalies in any sensor data that can be represented as a time series. The performance of this method is still to be proven on a large set of general sensor data, but it shows promise, mainly for sensor systems consisting of several sensor signals.
dc.identifier.urihttps://hdl.handle.net/20.500.12380/223871
dc.language.isoeng
dc.relation.ispartofseriesDiploma work - Department of Applied Mechanics, Chalmers University of Technology, Göteborg, Sweden : 2015:35
dc.setspec.uppsokTechnology
dc.subjectSignalbehandling
dc.subjectHållbar utveckling
dc.subjectInformations- och kommunikationsteknik
dc.subjectInnovation och entreprenörskap (nyttiggörande)
dc.subjectTransport
dc.subjectSignal Processing
dc.subjectSustainable Development
dc.subjectInformation & Communication Technology
dc.subjectInnovation & Entrepreneurship
dc.subjectTransport
dc.titleAnomaly Detection in Logged Sensor Data
dc.type.degreeExamensarbete för masterexamensv
dc.type.degreeMaster Thesisen
dc.type.uppsokH
local.programmeComplex adaptive systems (MPCAS), MSc
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