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Title page for ETD etd-07312013-170858


Type of Document Dissertation
Author Bartram, Gregory Walsh
URN etd-07312013-170858
Title System Health Diagnosis and Prognosis Using Dynamic Bayesian Networks
Degree PhD
Department Civil Engineering
Advisory Committee
Advisor Name Title
Prof. Sankaran Mahadevan Committee Chair
Prof. Gautam Biswas Committee Member
Prof. Mark P. McDonald Committee Member
Prof. Prodyot K. Basu Committee Member
Keywords
  • dynamic bayesian network
  • diagnosis
  • prognosis
  • decision making
  • particle filtering
Date of Defense 2013-07-18
Availability unrestricted
Abstract
This dissertation develops a methodology that provides information to make optimal decisions with respect to the mission and maintenance of a system. A great amount of information is needed about a system to make such decisions, including its current condition and predictions of its future state. The problem is broken into four subproblems. In the first, a dynamic Bayesian network based system modeling approach is developed for use when the available information is heterogeneous, i.e. available in various formats from various sources (e.g., laboratory data, operational data, expert opinion, mathematical models, and reliability data). The resulting system model accounts for uncertainty and is amenable to the system health management tasks of diagnosis, prognosis, and decision-making. In the second subproblem, a diagnosis approach for systems is developed for systems in the presence of heterogeneous information. The approach accounts for and quantifies uncertainty in the probability of damage, isolation, and quantification. Next, a prognosis is considered when the available information is heterogeneous. The prognosis methodology accounts for uncertainty in diagnosis and is subjected to validation. Finally, a methodology for decision-making problem is developed for problems where the available information is heterogeneous and when assigning multiple systems to multiple missions. The methodology accounts for uncertainty in diagnosis and prognosis. The system modeling, diagnosis, prognosis, and decision-making problems are illustrated using a hydraulic actuator with multiple possible faults, while the diagnosis problem is demonstrated using a cantilever beam with possible damage at the support or a mid-span crack. The proposed methodology is general and can be applied to many different systems.
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