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Michael J. Ricard and Cynthia Rudin. Harris, William Ray Massachusetts Institute of Technology. Operations Research Center. Massachusetts Institute of Technology. Operations Research Center Sloan School of Management 2015-09-17T19:07:12Z 2015-09-17T19:07:12Z 2015 2015 http://hdl.handle.net/1721.1/98718 920692242 Thesis: S.M., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2015. Cataloged from PDF version of thesis. Includes bibliographical references (pages 101-102). This thesis considers the problem of detecting anomalies in performance data for unmanned underwater vehicles(UUVs). UUVs collect a tremendous amount of data, which operators are required to analyze between missions to determine if vehicle systems are functioning properly. Operators are typically under heavy time constraints when performing this data analysis. The goal of this research is to provide operators with a post-mission data analysis tool that automatically identifies anomalous features of performance data. Such anomalies are of interest because they are often the result of an abnormal condition that may prevent the vehicle from performing its programmed mission. In this thesis, we consider existing one-class classification anomaly detection techniques since labeled training data from the anomalous class is not readily available. Specifically, we focus on two anomaly detection techniques: (1) Kernel Density Estimation (KDE) Anomaly Detection and (2) Local Outlier Factor. Results are presented for selected UUV systems and data features, and initial findings provide insight into the effectiveness of these algorithms. Lastly, we explore ways to extend our KDE anomaly detection algorithm for various tasks, such as finding anomalies in discrete data and identifying anomalous trends in time-series data. by William Ray Harris. S.M. 102 pages eng Massachusetts Institute of Technology M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 Operations Research Center. Anomaly detection methods for unmanned underwater vehicle performance data Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="7ee8bd43-cf9d-4005-a46a-e9bd1fc89bce"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Anomaly detection methods for unmanned underwater vehicle performance data</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2015</PublicationDate> <Authors> <Author> <DisplayName>Harris, William Ray</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Operations Research Center.</Keyword> <Abstract>This thesis considers the problem of detecting anomalies in performance data for unmanned underwater vehicles(UUVs). UUVs collect a tremendous amount of data, which operators are required to analyze between missions to determine if vehicle systems are functioning properly. Operators are typically under heavy time constraints when performing this data analysis. The goal of this research is to provide operators with a post-mission data analysis tool that automatically identifies anomalous features of performance data. Such anomalies are of interest because they are often the result of an abnormal condition that may prevent the vehicle from performing its programmed mission. In this thesis, we consider existing one-class classification anomaly detection techniques since labeled training data from the anomalous class is not readily available. Specifically, we focus on two anomaly detection techniques: (1) Kernel Density Estimation (KDE) Anomaly Detection and (2) Local Outlier Factor. Results are presented for selected UUV systems and data features, and initial findings provide insight into the effectiveness of these algorithms. Lastly, we explore ways to extend our KDE anomaly detection algorithm for various tasks, such as finding anomalies in discrete data and identifying anomalous trends in time-series data.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>