Robust Bayesian Anomaly Detection Methods for Large Scale Sensor Systems
| dc.contributor.author | Merkes, Sierra Nicole | en |
| dc.contributor.committeechair | Leman, Scott C. | en |
| dc.contributor.committeemember | Franck, Christopher Thomas | en |
| dc.contributor.committeemember | Higdon, David | en |
| dc.contributor.committeemember | Smith, Eric P. | en |
| dc.contributor.department | Statistics | en |
| dc.date.accessioned | 2022-09-13T08:00:14Z | en |
| dc.date.available | 2022-09-13T08:00:14Z | en |
| dc.date.issued | 2022-09-12 | en |
| dc.description.abstract | Sensor systems, such as modern wind tunnels, require continual monitoring to validate their quality, as corrupted data will increase both experimental downtime and budget and lead to inconclusive scientific and engineering results. One approach to validate sensor quality is monitoring individual sensor measurements' distribution. Although, in general settings, we do not know how to correct measurements should be distributed for each sensor system. Instead of monitoring sensors individually, our approach relies on monitoring the co-variation of the entire network of sensor measurements, both within and across sensor systems. That is, by monitoring how sensors behave, relative to each other, we can detect anomalies expeditiously. Previous monitoring methodologies, such as those based on Principal Component Analysis, can be heavily influenced by extremely outlying sensor anomalies. We propose two Bayesian mixture model approaches that utilize heavy-tailed Cauchy assumptions. First, we propose a Robust Bayesian Regression, which utilizes a scale-mixture model to induce a Cauchy regression. Second, we extend elements of the Robust Bayesian Regression methodology using additive mixture models that decompose the anomalous and non-anomalous sensor readings into two parametric compartments. Specifically, we use a non-local, heavy-tailed Cauchy component for isolating the anomalous sensor readings, which we refer to as the Modified Cauchy Net. | en |
| dc.description.abstractgeneral | Sensor systems, such as modern wind tunnels, require continual monitoring to validate their quality, as corrupted data will increase both experimental downtime and budget and lead to inconclusive scientific and engineering results. One approach to validate sensor quality is monitoring individual sensor measurements' distribution. Although, in general settings, we do not know how to correct measurements should be distributed for each sensor system. Instead of monitoring sensors individually, our approach relies on monitoring the co-variation of the entire network of sensor measurements, both within and across sensor systems. That is, by monitoring how sensors behave, relative to each other, we can detect anomalies expeditiously. We proposed two Bayesian monitoring approaches called the Robust Bayesian Regression and Modified Cauchy Net, which provide flexible, tunable models for detecting anomalous sensors with the historical data containing anomalous observations. | en |
| dc.description.degree | Doctor of Philosophy | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:35294 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/111805 | en |
| dc.language.iso | en | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | Anomaly Detection | en |
| dc.subject | Bayesian | en |
| dc.subject | Mixture Models | en |
| dc.subject | Process Control | en |
| dc.subject | Wind Tunne | en |
| dc.title | Robust Bayesian Anomaly Detection Methods for Large Scale Sensor Systems | en |
| dc.type | Dissertation | en |
| thesis.degree.discipline | Statistics | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | doctoral | en |
| thesis.degree.name | Doctor of Philosophy | en |
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