Title:
Sensor-based prognostics and structured maintenance policies for components with complex degradation

dc.contributor.advisor Gebraeel, Nagi
dc.contributor.author Elwany, Alaa H. en_US
dc.contributor.committeeMember White, Chelsea C., III
dc.contributor.committeeMember Wu, C. F. Jeff
dc.contributor.committeeMember Shi, Jianjun
dc.contributor.committeeMember Maillart, Lisa
dc.contributor.department Industrial and Systems Engineering en_US
dc.date.accessioned 2011-03-04T20:22:22Z
dc.date.available 2011-03-04T20:22:22Z
dc.date.issued 2009-09-23 en_US
dc.description.abstract We propose a mathematical framework that integrates low-level sensory signals from monitoring engineering systems and their components with high-level decision models for maintenance optimization. Our objective is to derive optimal adaptive maintenance strategies that capitalize on condition monitoring information to update maintenance actions based upon the current state of health of the system. We refer to this sensor-based decision methodology as "sense-and-respond logistics". As a first step, we develop and extend degradation models to compute and periodically update the remaining life distribution of fielded components using in situ degradation signals. Next, we integrate these sensory updated remaining life distributions with maintenance decision models to; (1) determine, in real-time, the optimal time to replace a component such that the lost opportunity costs due to early replacements are minimized and system utilization is increased, and (2) sequentially determine the optimal time to order a spare part such that inventory holding costs are minimized while preventing stock outs. Lastly, we integrate the proposed degradation model with Markov process models to derive structured replacement and spare parts ordering policies. In particular, we show that the optimal maintenance policy for our problem setting is a monotonically non-decreasing control limit type policy. We validate our methodology using real-world data from monitoring a piece of rotating machinery using vibration accelerometers. We also demonstrate that the proposed sense-and-respond decision methodology results in better decisions and reduced costs compared to other traditional approaches. en_US
dc.description.degree Ph.D. en_US
dc.identifier.uri http://hdl.handle.net/1853/37198
dc.publisher Georgia Institute of Technology en_US
dc.subject Replacement models en_US
dc.subject Spare parts inventory en_US
dc.subject Maintenance policies en_US
dc.subject Markov decision processes en_US
dc.subject Reliability en_US
dc.subject Degradation models en_US
dc.subject.lcsh Machinery Maintenance and repair
dc.subject.lcsh Plant performance Monitoring
dc.subject.lcsh Reliability (Engineering)
dc.title Sensor-based prognostics and structured maintenance policies for components with complex degradation en_US
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
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