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dc.contributor.authorHarris, Bradley Williamen_US
dc.date.accessioned2013-05-27T08:00:25Z
dc.date.available2013-05-27T08:00:25Z
dc.date.issued2013-05-26en_US
dc.identifier.othervt_gsexam:996en_US
dc.identifier.urihttp://hdl.handle.net/10919/23103
dc.description.abstractSymbolic dynamics is a current interest in the area of anomaly detection, especially in mechanical systems.  Symbolic dynamics reduces the overall dimensionality of system responses while maintaining a high level of robustness to noise.  Rolling element bearings are particularly common mechanical components where anomaly detection is of high importance.  Harsh operating conditions and manufacturing imperfections increase vibration innately reducing component life and increasing downtime and costly repairs.  This thesis presents a novel way to detect bearing vibrational anomalies through Symbolic Aggregate Approximation (SAX) in the two-dimensional time-frequency domain.  SAX reduces computational requirements by partitioning high-dimensional sensor data into discrete states.  This analysis specifically suits bearing vibration data in the time-frequency domain, as the distribution of data does not greatly change between normal and faulty conditions.
Under ground truth synthetically-generated experiments, two-dimensional SAX in conjunction with Markov model feature extraction is successful in detecting anomalies (> 99%) using short time spans (< 0.1 seconds) of data in the time-frequency domain with low false alarms (< 8%).  Analysis of real-world datasets validates the performance over the commonly used one-dimensional symbolic analysis by detecting 100% of experimental anomalous vibration with 0 false alarms in all fault types using less than 1 second of data for the basis of \'normality\'.  Two-dimensional SAX also demonstrates the ability to detect anomalies in predicative monitoring environments earlier than previous methods, even in low Signal-to-Noise ratios.
en_US
dc.format.mediumETDen_US
dc.publisherVirginia Techen_US
dc.rightsThis Item is protected by copyright and/or related rights. Some uses of this Item may be deemed fair and permitted by law even without permission from the rights holder(s), or the rights holder(s) may have licensed the work for use under certain conditions. For other uses you need to obtain permission from the rights holder(s).en_US
dc.subjectSymbolic dynamicsen_US
dc.subjectRolling element bearingsen_US
dc.subjectAnomaly detectionen_US
dc.subjectPredicative maintenanceen_US
dc.subjectDigital signal processingen_US
dc.subjectBayesian staen_US
dc.titleAnomaly detection in rolling element bearings via two-dimensional Symbolic Aggregate Approximationen_US
dc.typeThesisen_US
dc.contributor.departmentMechanical Engineeringen_US
dc.description.degreeMSen_US
thesis.degree.nameMSen_US
thesis.degree.levelmastersen_US
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen_US
thesis.degree.disciplineMechanical Engineeringen_US
dc.contributor.committeechairRoan, Michael Jen_US
dc.contributor.committeememberWest, Robert Len_US
dc.contributor.committeememberLeonessa, Alexanderen_US


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