Title:
Real-time Data Analytics for Condition Monitoring of Complex Industrial Systems
Real-time Data Analytics for Condition Monitoring of Complex Industrial Systems
| dc.contributor.advisor | Gebraeel, Nagi | |
| dc.contributor.author | Peters, Benjamin | |
| dc.contributor.committeeMember | Paynabar, Kamran | |
| dc.contributor.committeeMember | Serban, Nicoleta | |
| dc.contributor.committeeMember | Shi, Jianjun | |
| dc.contributor.committeeMember | Lieuwen, Timothy | |
| dc.contributor.department | Industrial and Systems Engineering | |
| dc.date.accessioned | 2022-01-14T16:12:41Z | |
| dc.date.available | 2022-01-14T16:12:41Z | |
| dc.date.created | 2021-12 | |
| dc.date.issued | 2021-12-14 | |
| dc.date.submitted | December 2021 | |
| dc.date.updated | 2022-01-14T16:12:42Z | |
| dc.description.abstract | Modern industrial systems are now fitted with several sensors for condition monitoring. This is advantageous because these sensors can provide mass amounts of data that have the potential for aiding in tasks such as fault detection, diagnosis, and prognostics. However, the information valuable for performing these tasks is often clouded in noise and must be mined from high-dimensional data structures. Therefore, this dissertation presents a data analytics framework for performing these condition monitoring tasks using high-dimensional data. Demonstrations of this framework are detailed for challenges related to power generation systems in automobiles, power plants, and aircraft engines. These implementations leverage data collected from state-of-the-art, industry class test-rigs. Results indicate the ability of this framework to develop effective methodologies for condition monitoring of complex systems. | |
| dc.description.degree | Ph.D. | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.uri | http://hdl.handle.net/1853/66158 | |
| dc.language.iso | en_US | |
| dc.publisher | Georgia Institute of Technology | |
| dc.subject | severity-based diagnosis, EPGS system, vehicle-engine start system, feature extraction, Regularized Multinomial Regression, ensemble methods, lean blowout, extinction, re-ignition, time series modeling, control charts, data driven methods, fault detection, swirl flames, condition monitoring, fluid dynamics, heat transfer, compressor, turbine, gas turbine engines, measurement techniques, infrared imaging, sparse feature selection, group variable selection, aircraft engine, penalized mixture of Gaussian regression, sensor selection, prognostics | |
| dc.title | Real-time Data Analytics for Condition Monitoring of Complex Industrial Systems | |
| dc.type | Text | |
| dc.type.genre | Dissertation | |
| dspace.entity.type | Publication | |
| local.contributor.advisor | Gebraeel, Nagi | |
| local.contributor.corporatename | H. Milton Stewart School of Industrial and Systems Engineering | |
| local.contributor.corporatename | College of Engineering | |
| relation.isAdvisorOfPublication | 7475bd6a-cb04-4f7f-a4b1-323201edc9e2 | |
| relation.isOrgUnitOfPublication | 29ad75f0-242d-49a7-9b3d-0ac88893323c | |
| relation.isOrgUnitOfPublication | 7c022d60-21d5-497c-b552-95e489a06569 | |
| thesis.degree.level | Doctoral |