Title:
Knowledge-Based Architecture for Integrated Condition Based Maintenance of Engineering Systems

Simple item page
dc.contributor.advisor Vachtsevanos, George J.
dc.contributor.author Saxena, Abhinav en_US
dc.contributor.committeeMember Egerstedt, Magnus
dc.contributor.committeeMember Ferri, Aldo A.
dc.contributor.committeeMember Howard, Ayanna
dc.contributor.committeeMember Saad, Ashraf
dc.contributor.department Electrical and Computer Engineering en_US
dc.date.accessioned 2007-08-16T17:36:51Z
dc.date.available 2007-08-16T17:36:51Z
dc.date.issued 2007-07-06 en_US
dc.description.abstract A paradigm shift is emerging in system reliability and maintainability. The military and industrial sectors are moving away from the traditional breakdown and scheduled maintenance to adopt concepts referred to as Condition Based Maintenance (CBM) and Prognostic Health Management (PHM). In addition to signal processing and subsequent diagnostic and prognostic algorithms these new technologies involve storage of large volumes of both quantitative and qualitative information to carry out maintenance tasks effectively. This not only requires research and development in advanced technologies but also the means to store, organize and access this knowledge in a timely and efficient fashion. Knowledge-based expert systems have been shown to possess capabilities to manage vast amounts of knowledge, but an intelligent systems approach calls for attributes like learning and adaptation in building autonomous decision support systems. This research presents an integrated knowledge-based approach to diagnostic reasoning for CBM of engineering systems. A two level diagnosis scheme has been conceptualized in which first a fault is hypothesized using the observational symptoms from the system and then a more specific diagnostic test is carried out using only the relevant sensor measurements to confirm the hypothesis. Utilizing the qualitative (textual) information obtained from these systems in combination with quantitative (sensory) information reduces the computational burden by carrying out a more informed testing. An Industrial Language Processing (ILP) technique has been developed for processing textual information from industrial systems. Compared to other automated methods that are computationally expensive, this technique manipulates standardized language messages by taking advantage of their semi-structured nature and domain limited vocabulary in a tractable manner. A Dynamic Case-based reasoning (DCBR) framework provides a hybrid platform for diagnostic reasoning and an integration mechanism for the operational infrastructure of an autonomous Decision Support System (DSS) for CBM. This integration involves data gathering, information extraction procedures, and real-time reasoning frameworks to facilitate the strategies and maintenance of critical systems. As a step further towards autonomy, DCBR builds on a self-evolving knowledgebase that learns from its performance feedback and reorganizes itself to deal with non-stationary environments. A unique Human-in-the-Loop Learning (HITLL) approach has been adopted to incorporate human feedback in the traditional Reinforcement Learning (RL) algorithm. en_US
dc.description.degree Ph.D. en_US
dc.identifier.uri http://hdl.handle.net/1853/16125
dc.publisher Georgia Institute of Technology en_US
dc.subject Human in the loop learning en_US
dc.subject Dynamic case-based reasoning en_US
dc.subject Industrial language processing en_US
dc.subject Evolving knowledgebase en_US
dc.subject Condition based maintenance en_US
dc.subject Knowledge management en_US
dc.subject.lcsh System failures (Engineering) en_US
dc.subject.lcsh Automatic test equipment en_US
dc.subject.lcsh Decision support systems en_US
dc.subject.lcsh Machinery Maintenance and repair en_US
dc.title Knowledge-Based Architecture for Integrated Condition Based Maintenance of Engineering Systems en_US
dc.type Text
dc.type.genre Dissertation
dspace.entity.type Publication
local.contributor.advisor Vachtsevanos, George J.
local.contributor.corporatename School of Electrical and Computer Engineering
local.contributor.corporatename College of Engineering
relation.isAdvisorOfPublication 44a9325c-ad69-4032-a116-fd5987b92d56
relation.isOrgUnitOfPublication 5b7adef2-447c-4270-b9fc-846bd76f80f2
relation.isOrgUnitOfPublication 7c022d60-21d5-497c-b552-95e489a06569
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Saxena_Abhinav_200708_phd.pdf
Size:
4.18 MB
Format:
Adobe Portable Document Format
Description:
Download
Collections
Theses and Dissertations