Bayesian Methods in Reliability (Session 8C) $150.00
This session will present many novel ideas and new applications of Bayesian approaches in reliability engineering, from risk assessment to design decision making to system reliability analysis to condition-based maintenance.

Moderator
Frank (Feng-Bin) Sun, Ph.D., Western Digital Corporation

Downwards Propagating: Bayesian Analysis of Complex On Demand Systems (Paper 8C1)
Christopher Jackson, Royal Australian Army and Ali Mosleh, Ph.D., University of Maryland at College Park
This paper aims to deal with multiple data sets from different levels of complex on-demand systems. The paper will propose a method for incorporating overlapping higher level and lower level data in a Bayesian construct in order to update component reliability information. The technique can then be used to allow coordinated evidence sets from various system levels to reveal as much information as possible, and hence allow sensor placement optimization.
Developmental Space-System Elicitation Techniques for Risk-Informed Design (Paper 8C2)
Benjamin J. Franzini, Amanda Verges, and Blake F. Putney, Valador Inc.
The expert elicitation technique discussed in this paper conveys a method of risk-informed design performed in support of NASA Lunar Surface Systems design that is guided by system design documents and based heavily on face-to-face designer interaction and elicitation. This approach has proven to be very efficient, as designers are closely engaged early in design cycles and forced to focus on reliability strategies that were heavily influenced and implemented by the designer’s own expertise.
RBF Distribution Reduces Likelihood Estimate Bias of Small Sample Size (Paper 8C3)
Moshe Felix Barmoav, Motorola
This paper presents a new method to address the likelihood estimates bias as a result of small sample size and the new distribution attributes and flexibility.
Qualitative-Quantitative Bayesian Belief Networks for Risk Assessment (Paper 8C4)
Chengdong Wang, Ali Mosleh, Ph.D., University of Maryland at College Park
This paper presents a new methodology combining the quantitative and qualitative Bayesian Belief Networks together to do the risk assessment and reliability analysis.