A Bayesian Model for Integrating Multiple Sources of Lifetime Information in System-Reliability Assessments

Reese, C. Shane; Wilson, Alyson G.; Guo, Jiqiang; Hamada, Michael S.; Johnson, Valen E.

doi:10.1080/00224065.2011.11917851

Cited by 42 publications

(17 citation statements)

References 40 publications

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“…All such data should be incorporated in a system assessment. Johnson et al , Reese et al , and Guo and Wilson show how independent multi‐level pass/fail data, independent multi‐level failure time data, and independent multi‐level degradation data can be analyzed, respectively. Graves et al show how simultaneous multi‐level pass/fail data can be incorporated properly.…”

Section: Introductionmentioning

confidence: 99%

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A Note on Incorporating Simultaneous Multi‐level Failure Time Data in System Reliability Assessments

Graves

Hamada

2015

Quality & Reliability Eng

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In this article, we present a method how to evaluate the likelihood for simultaneous failure time data when monitoring is stopped when the system fails. Our method is based on the reliability structure of the system, listing all possible events consistent with the simultaneous data and calculating their contributions to the likelihood. The method is simple to understand and is based on standard probabilistic calculations. We also consider the Jackson and Mosleh 5 method, and some results suggest that the two methods are equivalent.

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Section: Introductionmentioning

confidence: 99%

“…Because other articles have shown how to analyze various types of reliability data using a Bayesian approach (Johnson et al , Reese et al , Guo and Wilson , Graves et al ), here, we only consider what contributions that simultaneous failure time data make to the likelihood or data model. An outline of this note is as follows.…”

Section: Introductionmentioning

confidence: 99%

A Note on Incorporating Simultaneous Multi‐level Failure Time Data in System Reliability Assessments

Graves

Hamada

2015

Quality & Reliability Eng

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“…Analysis of system level data has also been developed in [26][27][28]. Reese et al developed a method for the analysis of nonoverlapping data for time based systems [29] allowing the integration of component, sub-system and system-level data. Many principles of this method are shared with the methodologies presented in this paper.…”

Section: ð3-11þmentioning

confidence: 98%

“…Many principles of this method are shared with the methodologies presented in this paper. The method contained in [29] does not include examples where sub-system lifetime data is analyzed (beyond one instance of expert opinion), but can easily accommodate such a scenario.…”

Section: ð3-11þmentioning

confidence: 99%

Bayesian inference with overlapping data: Reliability estimation of multi-state on-demand continuous life metric systems with uncertain evidence

Jackson

Mosleh

2016

Reliability Engineering & System Safety

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“…Hamada et al (2008) presented a methodology using Bayesian approach to induct pass/fail, lifetime, and degradation data into system reliability analysis. Reliability assessment using lifetime data sets have also been investigated by Reese et al (2011).…”

Section: Data Integration Into Hierarchical Toolsmentioning

confidence: 99%

Risk and reliability assessment in chemical process industries using Bayesian methods

Roy

Srivastava

Sinha

2014

Reviews in Chemical Engineering

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Chemical process industries (CPI) are usually home to a large number of complex systems and components required for various operations involving hazardous chemicals. The intense operating conditions and complex interactions between the systems make the chemical plants vulnerable to accidents. Quotidian incidents and mishaps can lead to catastrophic incidents. Thus, the area of risk assessment and reliability analysis in CPI has been of much interest to the research community. The complexity of processes in CPI demands a risk assessment tool that can adapt itself to the dynamic environment and can efficiently model the functional and sequential dependencies between the components and the effects of external factors, component degradation, and variation in operating conditions. The risk assessment tool must have applicability during the operational lifetime of the system to serve as a platform for decision making and risk management. The unavailability of empirical data for some variables is another pertinent issue in risk analysis and reliability assessment in CPI. Analysts often have to work with subjective information such as expert opinion. Bayesian statistical methods based on the Bayes theorem are considered by many to be an effective tool to address the above-mentioned issues. These methods are based on the subjective interpretation of probability that helps to model the epistemic uncertainties and easily propagates them through complex system models. The methods provide a formal systematic way to incorporate subjective information into calculations. The inherent updating property accoutres these methods with the ability to deal with real-time changes. The opponents, however, point out that the methods produce high overconfidence and randomness in computed answers. In the last two decades, an increased interest can be seen in the research community toward the use of Bayesian methods in risk assessment. This paper presents a comprehensive literature review of the application of Bayesian methods through Bayesian parameter estimation techniques and Bayesian updating procedures in process industries. Both these techniques have been extensively used in various aspects of risk analysis, which are very pertinent in CPI. The purpose of the study is to produce an effective reference guide for scholars interested in applying Bayesian techniques to risk and reliability assessment in CPI. AbbreviationsAI artificial intelligence ASP accident sequence precursor BN Bayesian network CCF common cause failure CPI chemical process industry CPT conditional probability table CTBN continuous time Bayesian network DAG directed acyclic graph DBN dynamic Bayesian network DCS distributed control system DDT diagnostic decision tree DIF diagnostic importance factor DOOBN dynamic object-oriented Bayesian network DRA dynamic risk assessment DTBN discrete time Bayesian network EB empirical Bayes ESD emergency shutdown EVT extreme value theorem ET event tree FB fully Bayes FMEA failure mode effects analysis FFT fast Fourier transform FT fa...

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A Bayesian Model for Integrating Multiple Sources of Lifetime Information in System-Reliability Assessments

Cited by 42 publications

References 40 publications

A Note on Incorporating Simultaneous Multi‐level Failure Time Data in System Reliability Assessments

A Note on Incorporating Simultaneous Multi‐level Failure Time Data in System Reliability Assessments

Bayesian inference with overlapping data: Reliability estimation of multi-state on-demand continuous life metric systems with uncertain evidence

Risk and reliability assessment in chemical process industries using Bayesian methods

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