TITLE

Improving Reliability Estimates with Bayesian Statistics

AUTHOR(S)
Fronczyk, Kassandra; Freeman, Laura
PUB. DATE
December 2016
SOURCE
ITEA Journal of Test & Evaluation;Dec2016, Vol. 37 Issue 4, p298
SOURCE TYPE
Periodical
DOC. TYPE
Article
ABSTRACT
There are many challenges with assessing the reliability of a complex system. One of the more difficult aspects of system reliability assessment is integrating multiple sources of information, including component, subsystem, and full system data, as well as possible previous test data or subject matter expert (SME) knowledge. The Bayesian paradigm is tailor made for these types of situations, allowing for the combination of multiple sources of data and variability to obtain more robust reliability estimates and uncertainty quantification. The Bayesian approach to combining reliabilities is discussed through a recent multi-mission ship example. The Bayesian approach to combining information from various subsystems/components and other sources to estimate full system reliability has many advantages. Most notably, multiple sources of prior information can be incorporated, complex systems (and their structures) can be analyzed without seriously increasing the computations, and uncertainty intervals are straight-forward to calculate and interpret.
ACCESSION #
120515576

 

Related Articles

  • Optimization of plasma diagnostics using Bayesian probability theory. Dreier, H.; Fischer, R.; Dinklage, A.; Hirsch, M.; Kornejew, P. // AIP Conference Proceedings;2006, Vol. 872 Issue 1, p304 

    The diagnostic set-up for Wendelstein 7-X, a magnetic fusion device presently under construction, is currently in the design process to optimize the outcome under given technical constraints. Compared to traditional design approaches, Bayesian Experimental Design (BED) allows to optimize with...

  • Bayesian calibration, validation, and uncertainty quantification of diffuse interface models of tumor growth. Hawkins-Daarud, Andrea; Prudhomme, Serge; van der Zee, Kristoffer G.; Oden, J. Tinsley // Journal of Mathematical Biology;Dec2013, Vol. 67 Issue 6/7, p1457 

    The idea that one can possibly develop computational models that predict the emergence, growth, or decline of tumors in living tissue is enormously intriguing as such predictions could revolutionize medicine and bring a new paradigm into the treatment and prevention of a class of the deadliest...

  • Statistical Quantification of Methylation Levels by Next-Generation Sequencing. Guodong Wu; Nengjun Yi; Absher, Devin; Degui Zhi // PLoS ONE;2011, Vol. 6 Issue 6, p1 

    Background/Aims: Recently, next-generation sequencing-based technologies have enabled DNA methylation profiling at high resolution and low cost. Methyl-Seq and Reduced Representation Bisulfite Sequencing (RRBS) are two such technologies that interrogate methylation levels at CpG sites throughout...

  • Calibration and intercomparison of acetic acid measurements using proton transfer reaction mass spectrometry (PTR-MS). Haase, K. B.; Keene, W. C.; Pszenny, A. A. P.; Mayne, H. R.; Talbot, R. W.; Sive, B. C. // Atmospheric Measurement Techniques Discussions;2012, Vol. 5 Issue 4, p4635 

    The article presents a study on the validity of Proton Transfer Reaction Mass Spectrometry (PTR-MS) to quantify acetic acid vapor. The study uses permeation tubes calibrated at low parts per billion by volume (ppbv) mixing ratios to evaluate three different PTR-MS configurations. It adds that...

  • Bayesian Inference for Some Mixture Problems in Quality and Reliability. Nair, Vijayan N.; Tang, Boxin // Journal of Quality Technology;Jan2001, Vol. 33 Issue 1, p16 

    Considers Bayesian inference for three important classes of problems that arise in quality and reliability applications. Overview on the data augmentation method for Bayesian inference in the context of finite mixture models; Basic idea behind data augmentation; Analysis on manufacturing defect...

  • Operational Risk Factors Assessment in non-nominally and transitory running regimes for Nuclear Power Plants. Tonţ, Gabriela; Vlădăreanu, Luige; Munteanu, Mihai Stelian; Tonţ, Dan George // Journal of Electrical & Electronics Engineering;Jun2010, Vol. 3 Issue 1, p227 

    Using interdisciplinary approach and modeling techniques the paper bridged the disciplinary viewpoints towards supporting the system in operation with the aim of facilitating effective decision-making during the task. The paper focuses on the modeling risk using Bayesian associated analysis...

  • Bayesian system reliability and availability analysis under the vague environment based on Exponential distribution. Gholizadeh, Ramin; Shirazi, Aliakbar M.; Hadian, Maghdoode // International Journal on Soft Computing;Feb2012, Vol. 3 Issue 1, p25 

    Reliability modeling is the most important discipline of reliable engineering. The main purpose of this paper is to provide a methodology for discussing the vague environment. Actually we discuss on Bayesian system reliability and availability analysis on the vague environment based on...

  • RELIABILITY AND DIAGNOSTIC OF MODULAR SYSTEMS. Kohlas, Jurg; Anrig, Bernhard; Bissig, Roman // Orion;2000, Vol. 16 Issue 1, p47 

    Shows how the computation of reliability and diagnostic can efficiently be done within the same Bayesian network induced by the modularity of the structure function of a modular system. Overview of computational architectures for Bayesian networks; General representations of any monotone...

  • Palmprint Recognition: A Naïve Bayesian Approach. Nisar, Zanobya; Jan, Zahoor; Khan, Rehanullah; Qureshi, Rashid Jalal // World Applied Sciences Journal;2014, Vol. 31 Issue 5, p771 

    Identification of individuals via palmprint based biometric system is becoming very popular due to its reliability and high performance as it is enriched with several unique and stable features. In this article, we present a novel, convex hull oriented feature based machine learning approach for...

  • Research on Evaluation Method Based on Modified Buckley Decision Making and Bayesian Network. Yang, Neng-pu; Han, Mei; Chen, Shi-yong; Liu, Xiao-hua; Kang, Liu-jiang // Mathematical Problems in Engineering;105/2015, Vol. 2015, p1 

    This work presents a novel evaluation method, which can be applied in the field of risk assessment, project management, cause analysis, and so forth. Two core technologies are used in the method, namely, modified Buckley Decision Making and Bayesian Network. Based on the modified Buckley...

Share

Read the Article

Courtesy of NEW JERSEY STATE LIBRARY

Sorry, but this item is not currently available from your library.

Try another library?
Sign out of this library

Other Topics