Master of Science Thesis Defense: Anusha Krishna Murthy
When: Tuesday,
December 13, 2016
12:00 PM
-
2:00 PM
Where: Science & Engineering Building, Lester W. Cory Conference Room: Room 213A
Cost: Free
Description: TOPIC: RELIABILITY AND COST OF TWO FAILURE MODE SYSTEMS
SUBJECT TO CORRELATED FAILURES AND EXPECTATION CONDITIONAL MAXIMIZATION ALGORITHMS FOR FAILURE COUNT NON-HOMOGENEOUS POISSON PROCESS SOFTWARE RELIABILITY MODELS
LOCATION: Lester W. Cory Conference Room
Science & Engineering Building (SENG), Room 213A
ABSTRACT:
This thesis presents two contributions to the reliability engineering research literature: (i) reliability and cost expressions for systems subject to two modes of failure considering correlated component failures and expressions for reliability and cost optimal designs and (ii) expectation conditional maximization (ECM) algorithms to identify the maximum likelihood estimates (MLE) of nonhomogeneous Poisson process (NHPP) software reliability growth models (SRGM). Contribution (i) extends traditional expressions that assume component failures are independent and identically distributed (IID). Contribution (ii) extend ECM algorithms from failure time to failure count data.
Models to explicitly consider correlation are especially important for two failure modes reliability and cost modeling because the probability of open and closed mode failures are competing constraints that could lead to suboptimal designs when systems are susceptible to correlated failure. Failure count NHPP SRGM are most applicable to the largest and most complex software engineering projects that only record the number of failures observed in a specified interval instead of the individual failure times.
For each of the four two failure mode systems considered, we provide reliability and cost expressions as well as closed-form solutions for reliability and cost optimal designs, examining the impact of correlation in each failure mode on these optimal designs. The ECM algorithm for NHPP SRGM is applied in the context of the Weibull SRGM, which is a flexible and therefore widely applied model.
NOTE: All ECE Graduate Students are ENCOURAGED to attend.
All interested parties are invited to attend. Open to the public.
Advisor: Dr. Lance Fiondella
Committee Members: Dr. Liudong Xing, Department of Electrical & Computer Engineering and Dr. Gaurav Khanna, Physics Department
*For further information, please contact Dr. Lance Fiondella at 508.999.8596, or via email at lfiondella@umassd.edu.
SUBJECT TO CORRELATED FAILURES AND EXPECTATION CONDITIONAL MAXIMIZATION ALGORITHMS FOR FAILURE COUNT NON-HOMOGENEOUS POISSON PROCESS SOFTWARE RELIABILITY MODELS
LOCATION: Lester W. Cory Conference Room
Science & Engineering Building (SENG), Room 213A
ABSTRACT:
This thesis presents two contributions to the reliability engineering research literature: (i) reliability and cost expressions for systems subject to two modes of failure considering correlated component failures and expressions for reliability and cost optimal designs and (ii) expectation conditional maximization (ECM) algorithms to identify the maximum likelihood estimates (MLE) of nonhomogeneous Poisson process (NHPP) software reliability growth models (SRGM). Contribution (i) extends traditional expressions that assume component failures are independent and identically distributed (IID). Contribution (ii) extend ECM algorithms from failure time to failure count data.
Models to explicitly consider correlation are especially important for two failure modes reliability and cost modeling because the probability of open and closed mode failures are competing constraints that could lead to suboptimal designs when systems are susceptible to correlated failure. Failure count NHPP SRGM are most applicable to the largest and most complex software engineering projects that only record the number of failures observed in a specified interval instead of the individual failure times.
For each of the four two failure mode systems considered, we provide reliability and cost expressions as well as closed-form solutions for reliability and cost optimal designs, examining the impact of correlation in each failure mode on these optimal designs. The ECM algorithm for NHPP SRGM is applied in the context of the Weibull SRGM, which is a flexible and therefore widely applied model.
NOTE: All ECE Graduate Students are ENCOURAGED to attend.
All interested parties are invited to attend. Open to the public.
Advisor: Dr. Lance Fiondella
Committee Members: Dr. Liudong Xing, Department of Electrical & Computer Engineering and Dr. Gaurav Khanna, Physics Department
*For further information, please contact Dr. Lance Fiondella at 508.999.8596, or via email at lfiondella@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