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You searched for +publisher:"Vanderbilt University" +contributor:("Kai Goebel"). Showing records 1 – 2 of 2 total matches.

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Vanderbilt University

1. Zhang, Xiaoge. Machine Learning and Optimization Models to Assess and Enhance System Resilience.

Degree: PhD, Civil Engineering, 2019, Vanderbilt University

Engineering systems following disruptive events usually experience abrupt performance degradation over time. How to mitigate the disastrous effect of unanticipated events and restore the performance of the system to the original level is worthy of investigation. In this dissertation, we leverage machine learning and optimization techniques to investigate a variety of measures that can be taken before, during, and after the occurrence of extreme events to assess and enhance system resilience. Towards this end, six individual objectives are pursued: I. Prior to the occurrence of a hazardous event, we develop data-driven models to forecast when the hazardous event might occur in the future, thereby increasing stakeholderâs situation awareness; II. If a system malfunction has already happened, a hybrid model that blends multiple classification models is developed to predict the severity associated with the consequences in terms of their risk levels; III. Since operatorsâ experience and prior training plays a significant role in diagnosing and responding to off-nominal events, we develop a machine learning framework to measure the reliability of human operators in responding to malfunction events, based on multiple types of data collected from a human-in-the-loop experimental study; IV. Simulation data is used to characterize the performance of algorithmic response in managing an abnormal event; V. We investigate a design-for-resilience methodology, focusing on number and locations of service centers that respond to a disastrous event; VI. We also investigate a system reconfiguration strategy for resilient response to the increased demand caused by an extreme event. A variety of machine learning and optimization models are investigated in this dissertation to accomplish the above objectives. Along the machine learning front, a multi-fidelity deep learning model is developed to forecast system behavior over time, thereby enabling early warning regarding the occurrence of system hazards; the model accounts for variability in the data and uncertainty in the prediction. In addition, a hybrid model that blends support vector machine and ensemble of deep neural networks is trained to predict the consequence of abnormal events. Finally, support vector machine-based models are constructed to assess the performance of human and algorithmic responses to hazardous events. With respect to system resilience enhancement, we leverage a multi-level cross-entropy algorithm to tackle the formulated NP-hard bi-level optimization problems, where the samples in the cross-entropy algorithm adaptively converge to near-optimal solution within limited time. This algorithm is used to optimize the design of a logistics service center distribution by accounting for the potential impact of natural disasters, as well as to optimize the reconfiguration of an already existing traffic network to mitigate the system-wide congestion caused by large-volume evacuation out of a disaster-hit area. Advisors/Committee Members: Hiba Baroud (committee member), Mark Ellingham (committee member), Gautam Biswas (committee member), Sankaran Mahadevan (chair), Kai Goebel (committee member), Shankar Sankararaman (committee member).

Subjects/Keywords: Reliability analysis; Data analytics; Machine learning; Resilience optimization

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Zhang, X. (2019). Machine Learning and Optimization Models to Assess and Enhance System Resilience. (Doctoral Dissertation). Vanderbilt University. Retrieved from http://etd.library.vanderbilt.edu/available/etd-05072019-144332/ ;

Chicago Manual of Style (16th Edition):

Zhang, Xiaoge. “Machine Learning and Optimization Models to Assess and Enhance System Resilience.” 2019. Doctoral Dissertation, Vanderbilt University. Accessed July 20, 2019. http://etd.library.vanderbilt.edu/available/etd-05072019-144332/ ;.

MLA Handbook (7th Edition):

Zhang, Xiaoge. “Machine Learning and Optimization Models to Assess and Enhance System Resilience.” 2019. Web. 20 Jul 2019.

Vancouver:

Zhang X. Machine Learning and Optimization Models to Assess and Enhance System Resilience. [Internet] [Doctoral dissertation]. Vanderbilt University; 2019. [cited 2019 Jul 20]. Available from: http://etd.library.vanderbilt.edu/available/etd-05072019-144332/ ;.

Council of Science Editors:

Zhang X. Machine Learning and Optimization Models to Assess and Enhance System Resilience. [Doctoral Dissertation]. Vanderbilt University; 2019. Available from: http://etd.library.vanderbilt.edu/available/etd-05072019-144332/ ;

2. Kulkarni, Chetan Shrikant. A physics-based degradation modeling framework for diagnostic and prognostic studies in electrolytic capacitors.

Degree: PhD, Electrical Engineering, 2013, Vanderbilt University

Avionics systems play a critical role in many aspects of aircraft flight control. As the complexity of these systems increase, the chances of in-flight malfunctions are also likely to increase. This drives the need for Integrated Vehicle Health Management (IVHM) technologies for flight-critical avionics. Studying and analyzing the performance degradation of embedded electronics in the aircraft domain will help to increase aircraft reliability, assure in-flight performance, and reduce maintenance costs. Further, an understanding of how components degrade as well as the capability to anticipate failures and predict the remaining useful life (RUL) can provide a framework for condition-based maintenance. To support a condition-based maintenance and a safety-critical analysis framework, this thesis conducts a detailed study of the degradation mechanisms of electrolytic capacitors, an important component of most electronic systems. Electrolytic capacitors are known to have lower reliability than other electronic components that are used in power supplies of avionics equipment and electrical drivers of electro-mechanical actuators of control surfaces. Therefore, condition-based health assessment that leverages the knowledge of the device physics to model the degradation process can provide a generalized approach to predict remaining useful life as a function of current state of health and anticipated future operational and environmental conditions. We adopt a combined model and data-driven (experimental studies) approach to develop physics-based degradation modeling schemes for electrolytic capacitors. This approach provides a framework for tracking degradation and developing dynamic models to estimate the RUL of capacitors. The prognostics and RUL methodologies are based on a Bayesian tracking framework using the Kalman filter and Unscented Kalman filter approaches. The thesis makes contributions to physics-based modeling and a model-based prognostics methodology for electrolytic capacitors. Results discuss prognostics performance metrics like the median relative accuracy and the á-ë (alpha-lambda) accuracy. We have also demonstrated the derived physics-based degradation model is general, and applied to both accelerated and nominal degradation phenomena. Our overall results are accurate and robust, and, therefore, they can form the basis for condition-based maintenance and performance-based evaluation of complex systems. Advisors/Committee Members: Dr. Gabor Karsai (committee member), Dr. Mitchell Wilkes (committee member), Dr. Sankaran Mahadevan (committee member), Dr. Gautam Biswas (chair), Dr. Xenofon Koutsoukos (committee member), Dr. Kai Goebel (committee member), Dr. José Celaya (committee member).

Subjects/Keywords: Prognostics; Degradation. Avinonics Systems; Physics-based models; Electrolytic Capacitors; Accelerated Aging

Vanderbilt University and the Institute of Software Integrated Systems for giving me this… 

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Kulkarni, C. S. (2013). A physics-based degradation modeling framework for diagnostic and prognostic studies in electrolytic capacitors. (Doctoral Dissertation). Vanderbilt University. Retrieved from http://etd.library.vanderbilt.edu/available/etd-01142013-172424/ ;

Chicago Manual of Style (16th Edition):

Kulkarni, Chetan Shrikant. “A physics-based degradation modeling framework for diagnostic and prognostic studies in electrolytic capacitors.” 2013. Doctoral Dissertation, Vanderbilt University. Accessed July 20, 2019. http://etd.library.vanderbilt.edu/available/etd-01142013-172424/ ;.

MLA Handbook (7th Edition):

Kulkarni, Chetan Shrikant. “A physics-based degradation modeling framework for diagnostic and prognostic studies in electrolytic capacitors.” 2013. Web. 20 Jul 2019.

Vancouver:

Kulkarni CS. A physics-based degradation modeling framework for diagnostic and prognostic studies in electrolytic capacitors. [Internet] [Doctoral dissertation]. Vanderbilt University; 2013. [cited 2019 Jul 20]. Available from: http://etd.library.vanderbilt.edu/available/etd-01142013-172424/ ;.

Council of Science Editors:

Kulkarni CS. A physics-based degradation modeling framework for diagnostic and prognostic studies in electrolytic capacitors. [Doctoral Dissertation]. Vanderbilt University; 2013. Available from: http://etd.library.vanderbilt.edu/available/etd-01142013-172424/ ;

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