University Events

Description: TOPIC: RELIABILITY MODELING OF PROBABILISTIC COMPETING FAILURES

LOCATION: Lester W. Cory Conference Room, Science & Engineering Building (Group II), Room 213A

ABSTRACT:
Reliability modeling and analysis are essential for safe and reliable operation of complex systems and networks in modern and evolving technologies. This dissertation research concentrates on modeling effects of probabilistic competing failures in reliability analysis of critical systems with functional dependence behavior, where operations of some system components (referred to as dependent components) rely on functions of other components (referred to as trigger components) in a probabilistic manner. A failure of the trigger component may cause two-fold effects. On one hand, if the trigger component failure occurs first, the corresponding dependent components may be isolated with certain probabilities; such an isolation effect can prevent the system function from being compromised by further failures of those dependent components. On the other hand, if any of the dependent components experiences a propagated failure that occurs before the trigger component failure, the entire system can fail due to the propagation effect. In summary, competitions exist in the time domain between the failure propagation and probabilistic isolation effects, which can make significant contributions to the overall system reliability. Such competitions however have not been addressed by existing studies. In this dissertation work, combinatorial and analytical methods are proposed to evaluate reliability of systems subject to the probabilistic competing failure effects. The proposed methods are applicable to arbitrary types of time-to-failure distributions for system components. Some complicated scenarios are modeled, including system components subject to multiple types of local failures that have different statistical relationships (independent, dependent, mutually-exclusive) with propagated failures, systems with multiple correlated probabilistic functional dependence groups, and systems subject to phased-mission requirements. Practical case studies of wireless sensor networks are performed to illustrate the proposed methodologies.

NOTE: All ECE Graduate Students are ENCOURAGED to attend.
All interested parties are invited to attend. Open to the public.

Committee Members: Dr. Dayalan Kasilingam, Department of Electrical & Computer Engineering; Dr. David W. Coit, Department of Industrial & Systems Engineering, Rutgers University

*For further information, please contact Dr. Liudong Xing or Dr. Honggang Wang at 508.999.8883 or 508.999.8469, or via email at lxing@umassd.edu or hwang1@umassd.edu