ECE Master of Science Thesis Defense By: Jean E. Pierre
When: Monday,
January 13, 2020
10:00 AM
-
12:00 PM
Where: Science & Engineering Building, Lester W. Cory Conference Room: Room 213A
Cost: Free
Description: Topic: A Monte Carlo Simulation Framework For Reliability Analysis Of Dynamic Standby Sparing Systems
Location: Lester W. Cory Conference Room, Science & Engineering Building (SENG), Room 213A
Abstract:
Standby redundancy is a widely-used technique to increase the reliability of many mission-critical or life-critical systems, such as spacecrafts, flight controls, nuclear power plants and monitoring systems. In the case of a failure occurring, the standby redundancy technique enables those systems to continue their intended function. Standby redundancy can introduce dynamics into the component failure rate and system failure behavior, posing difficulty in the system reliability modeling and analysis. While the existing analytical methods have various limitations (in, e.g. distribution types and computational complexity), Monte Carlo simulations (MCS) provide a powerful means to evaluate reliability of standby sparing systems. However, the existing works using the MCS method focus only on specific use cases. This thesis proposes a reliability analysis framework based on the MCS method to address this issue. The framework models fault trees using Extensible Markup Language and MongoDB to store the elements of the model. The MCS algorithm and the framework are implemented using Scala and Java and tested using three case studies. The framework also provides the capability to evaluate failure importance indices for system elements to indicate their criticality to the functionality of the system. This work also implements a user interface built on top of the framework for ease of use.
NOTE: All ECE Graduate Students are ENCOURAGED to attend.
All interested parties are invited to attend. Open to the public.
Advisor: Dr. Liudong Xing
Committee Members: Dr. Honggang Wang, Department of Electrical & Computer Engineering, UMASS Dartmouth;
Dr. Xiaoqin Shelley Zhang, Department of Computer & Information Science, UMASS Dartmouth
*For further information, please contact Dr. Liudong Xing at 508.999.8883, or via email at lxing@umassd.edu.
Location: Lester W. Cory Conference Room, Science & Engineering Building (SENG), Room 213A
Abstract:
Standby redundancy is a widely-used technique to increase the reliability of many mission-critical or life-critical systems, such as spacecrafts, flight controls, nuclear power plants and monitoring systems. In the case of a failure occurring, the standby redundancy technique enables those systems to continue their intended function. Standby redundancy can introduce dynamics into the component failure rate and system failure behavior, posing difficulty in the system reliability modeling and analysis. While the existing analytical methods have various limitations (in, e.g. distribution types and computational complexity), Monte Carlo simulations (MCS) provide a powerful means to evaluate reliability of standby sparing systems. However, the existing works using the MCS method focus only on specific use cases. This thesis proposes a reliability analysis framework based on the MCS method to address this issue. The framework models fault trees using Extensible Markup Language and MongoDB to store the elements of the model. The MCS algorithm and the framework are implemented using Scala and Java and tested using three case studies. The framework also provides the capability to evaluate failure importance indices for system elements to indicate their criticality to the functionality of the system. This work also implements a user interface built on top of the framework for ease of use.
NOTE: All ECE Graduate Students are ENCOURAGED to attend.
All interested parties are invited to attend. Open to the public.
Advisor: Dr. Liudong Xing
Committee Members: Dr. Honggang Wang, Department of Electrical & Computer Engineering, UMASS Dartmouth;
Dr. Xiaoqin Shelley Zhang, Department of Computer & Information Science, UMASS Dartmouth
*For further information, please contact Dr. Liudong Xing at 508.999.8883, or via email at lxing@umassd.edu.
Contact:
ECE: Electrical & Computer Engineering Department 508.999.9164 http://www.umassd.edu/engineering/ece/
Topical Areas: General Public, University Community, College of Engineering, Electrical and Computer Engineering