Abstract
For critical systems, timely recognition of an anomalous condition immediately starts the evaluation process. For complex systems, isolating the fault to a component or subsystem results in corrective action sooner so that undesired consequences may be minimized. There are many unique anomaly detection and fault isolation capabilities available with innovative techniques to quickly discover an issue and identify the underlying problems. This research develops a framework to aid in the selection of appropriate anomaly detection and fault isolation technology to augment a given system. To optimize this process, the framework employs a model based systems engineering approach. Specifically, a SysML model is generated that enables a system-level evaluation of alternative detection and isolation techniques, and subsequently identifies the preferable application(s) from these technologies A case study is conducted on a cryogenic liquid hydrogen system that was used to fuel the Space Shuttles at the Kennedy Space Center, Florida (and will be used to fuel the next generation Space Launch System rocket). This system is operated remotely and supports time-critical and highly hazardous operations making it a good candidate to augment with this technology. As the process depicted by the framework down-selects to potential applications for consideration, these too are tested in their ability to achieve required goals.
Notes
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Graduation Date
2019
Semester
Spring
Advisor
Rabelo, Luis
Degree
Doctor of Philosophy (Ph.D.)
College
College of Engineering and Computer Science
Department
Industrial Engineering and Management Systems
Degree Program
Industrial Engineering
Format
application/pdf
Identifier
CFE0008112; DP0023251
URL
https://purls.library.ucf.edu/go/DP0023251
Language
English
Release Date
May 2020
Length of Campus-only Access
None
Access Status
Doctoral Dissertation (Open Access)
STARS Citation
Clark, Thomas, "A Framework to Develop Anomaly Detection/Fault Isolation Architecture Using System Engineering Principles" (2019). Electronic Theses and Dissertations. 6834.
https://stars.library.ucf.edu/etd/6834