Title:
Optimum Sensor Localization/Selection In A Diagnostic/Prognostic Architecture

dc.contributor.advisor Ferri, Bonnie H.
dc.contributor.author Zhang, Guangfan en_US
dc.contributor.committeeMember Ferri, Aldo A.
dc.contributor.committeeMember Frazier, A. Bruno
dc.contributor.committeeMember Michaels, Thomas E.
dc.contributor.committeeMember Vachtsevanos, George J.
dc.contributor.department Electrical and Computer Engineering en_US
dc.date.accessioned 2005-07-28T17:52:09Z
dc.date.available 2005-07-28T17:52:09Z
dc.date.issued 2005-02-17 en_US
dc.description.abstract Optimum Sensor Localization/Selection in A Diagnostic/Prognostic Architecture Guangfan Zhang 107 Pages Directed by Dr. George J. Vachtsevanos This research addresses the problem of sensor localization/selection for fault diagnostic purposes in Prognostics and Health Management (PHM)/Condition-Based Maintenance (CBM) systems. The performance of PHM/CBM systems relies not only on the diagnostic/prognostic algorithms used, but also on the types, location, and number of sensors selected. Most of the research reported in the area of sensor localization/selection for fault diagnosis focuses on qualitative analysis and lacks a uniform figure of merit. Moreover, sensor localization/selection is mainly studied as an open-loop problem without considering the performance feedback from the on-line diagnostic/prognostic system. In this research, a novel approach for sensor localization/selection is proposed in an integrated diagnostic/prognostic architecture to achieve maximum diagnostic performance. First, a fault detectability metric is defined quantitatively. A novel graph-based approach, the Quantified-Directed Model, is called upon to model fault propagation in complex systems and an appropriate figure-of-merit is defined to maximize fault detectability and minimize the required number of sensors while achieving optimum performance. Secondly, the proposed sensor localization/selection strategy is integrated into a diagnostic/prognostic system architecture while exhibiting attributes of flexibility and scalability. Moreover, the performance is validated and verified in the integrated diagnostic/prognostic architecture, and the performance of the integrated diagnostic/prognostic architecture acts as useful feedback for further optimizing the sensors considered. The approach is tested and validated through a five-tank simulation system. This research has led to the following major contributions: ??generalized methodology for sensor localization/selection for fault diagnostic purposes. ??quantitative definition of fault detection ability of a sensor, a novel Quantified-Directed Model (QDG) method for fault propagation modeling purposes, and a generalized figure of merit to maximize fault detectability and minimize the required number of sensors while achieving optimum diagnostic performance at the system level. ??novel, integrated architecture for a diagnostic/prognostic system. ??lidation of the proposed sensor localization/selection approach in the integrated diagnostic/prognostic architecture. en_US
dc.description.degree Ph.D. en_US
dc.format.extent 1625998 bytes
dc.format.mimetype application/pdf
dc.identifier.uri http://hdl.handle.net/1853/6846
dc.language.iso en_US
dc.publisher Georgia Institute of Technology en_US
dc.subject Sensor selection
dc.subject Diagnostics
dc.subject Prognostics
dc.subject Sensor localization en_US
dc.subject.lcsh System failures (Engineering) en_US
dc.subject.lcsh Fault location (Engineering) en_US
dc.subject.lcsh Detectors en_US
dc.subject.lcsh Automatic test equipment en_US
dc.title Optimum Sensor Localization/Selection In A Diagnostic/Prognostic Architecture en_US
dc.type Text
dc.type.genre Dissertation
dspace.entity.type Publication
local.contributor.advisor Ferri, Bonnie H.
local.contributor.corporatename School of Electrical and Computer Engineering
local.contributor.corporatename College of Engineering
relation.isAdvisorOfPublication e8b8974b-0988-4c56-ae82-fbf253466591
relation.isOrgUnitOfPublication 5b7adef2-447c-4270-b9fc-846bd76f80f2
relation.isOrgUnitOfPublication 7c022d60-21d5-497c-b552-95e489a06569
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