Title:
Optimum Sensor Localization/Selection In A Diagnostic/Prognostic Architecture
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 |
Files
Original bundle
1 - 1 of 1
- Name:
- Zhang_Guangfan_200505_phd.pdf
- Size:
- 1.55 MB
- Format:
- Adobe Portable Document Format
- Description: