Session: 8C, Tuesday, 26 January 2010, 1545-1745 Moderator: Frank (Feng-Bin) Sun, Ph.D., Western Digital Corporation
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.
Papers: 8C1 [0220] DOWNWARDS PROPAGATING: BAYESIAN ANALYSIS OF COMPLEX ON DEMAND SYSTEMS by 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.
8C2 [0199] DEVELOPMENTAL SPACE-SYSTEM ELICITATION TECHNIQUES FOR RISK-INFORMED DESIGN by 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.
8C3 [0256] RBF DISTRIBUTION REDUCES LIKELIHOOD ESTIMATE BIAS OF SMALL SAMPLE SIZE by 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.
8C4 [0223] Qualitative-Quantitative Bayesian Belief Networks for Risk Assessment by 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.
Bayesian Methods in Reliability
Session: 8C, Tuesday, 26 January 2010, 1545-1745
Moderator: Frank (Feng-Bin) Sun, Ph.D., Western Digital Corporation
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.
Papers:
8C1 [0220] DOWNWARDS PROPAGATING: BAYESIAN ANALYSIS OF COMPLEX ON DEMAND SYSTEMS by 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.
8C2 [0199] DEVELOPMENTAL SPACE-SYSTEM ELICITATION TECHNIQUES FOR RISK-INFORMED DESIGN by 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.
8C3 [0256] RBF DISTRIBUTION REDUCES LIKELIHOOD ESTIMATE BIAS OF SMALL SAMPLE SIZE
by 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.
8C4 [0223] Qualitative-Quantitative Bayesian Belief Networks for Risk Assessment
by 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.