The combined use of data and expert estimates in population variability analysis

Droguett, Enrique López; Groen, Frank; Mosleh, Ali

doi:10.1016/j.ress.2003.10.007

Cited by 36 publications

(15 citation statements)

References 14 publications

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“…The latter situation occurs when an expert's opinion is about an item different from the one for which exposure evidence is available. This class of mixed models is topic of current research and a detailed discussion can be found in Droguett, Groen & Mosleh (2004).…”

Section: Discussionmentioning

confidence: 99%

“…In order to circumvent these limitations of both Bayesian and non-fully Bayesian approaches, Droguett, Groen & Mosleh (2004) proposed a fully Bayesian population variability analysis methodology that makes possible the combined use of run-time data (e.g., failures and operational time) and expert opinions resulting in a set of mixed likelihood models. However, this approach is only discussed in the context of these mixed likelihood models and there is no attempt to develop models for cases where either run-time data or expertbased evidence is available.…”

Section: Introductionmentioning

confidence: 99%

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Bayesian assessment of the variability of reliability measures

2006

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Population variability analysis, also known as the first stage in two-stage Bayesian updating, is an estimation procedure for the assessment of the variability of reliability measures among a group of subpopulations of similar systems. The estimated variability distributions are used as prior distributions in system-specific Bayesian updates. In this paper we present a Bayesian approach for population variability analysis involving the use of non-conjugate variability models that works over a continuous, rather than the discretized, variability model parameter space. The cases to be discussed are the ones typically encountered by the reliability practitioner: run-time data for failure rate assessment, demandbased data for failure probability assessment, and expert-based evidence for failure rate and failure probability analysis. We outline the estimation procedure itself as well as its link with conventional Bayesian updating procedures, describe the results generated by the procedures and their behavior under various data conditions, and provide numerical examples.Keywords: Bayes' theorem; population variability; reliability. ResumoAnálise de variabilidade populacional, também conhecida como o primeiro estágio no processo de atualização Bayesiana em dois estágios, é um procedimento de estimação utilizado para a quantificação da variabilidade de métricas de confiabilidade num conjunto de sub-populações de sistemas similares. As distribuições de variabilidade obtidas são usadas como distribuições a priori em atualizações Bayesianas específicas para um sistema. Neste artigo, apresenta-se um procedimento Bayesiano para a análise da variabilidade populacional envolvendo o uso de modelos de variabilidade não-conjugados que utilizam um espaço contínuo, ao invés de discreto, dos parâmetros do modelo de variabilidade. Discutem-se casos tipicamente encontrados na prática: dados de falha no tempo, dados de falha sob demanda e opiniões de especialistas para a estimação da taxa e probabilidade de falha. O procedimento de estimação é discutido, estabelece-se sua ligação com procedimentos convencionais de atualização Bayesiana e descrevem-se resultados gerados e o comportamento dos mesmos sob diferentes condições de dados, apresentando-se também exemplos numéricos.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Bayesian assessment of the variability of reliability measures

2006

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“…There has been an increase trend in the application of Bayesian networks in fields related to reliability, safety and maintenance (Mahadevan et al, 2001). Bayesian approaches to aggregate expert judgments on probabilities have been extensively investigated in risk and reliability analysis (Mosleh, 1986;Droguett et al, 2004;Podofillini and Dang, 2013). BNs provide a framework for addressing many of the shortcomings of human reliability analysis from a researcher perspective and from a practitioner perspective (Boring et al, 2010;Groth, Swiler, 2013).…”

Section: Bayesian Belief Network Modellingmentioning

confidence: 99%

“…The information obtained from these experts should be combined in a formal way. Clemen and Winkler (1999), Droguett, Groen and Mosleh (2004) claim that formal procedures are increasingly applied to elicit the opinion of specialists. If the data are obtained systematically from well-informed experts in primary and secondary processes, the opinion of experts can offer acceptable precision in quantification.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic risk analysis in manufacturing situational operation: application of modelling techniques and causal structure to improve safety performance.

Pereira

Lima

2015

Int. J. Prod. Manag. Eng.

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The use of probabilistic risk analysis in jet engines manufacturing process is essential to prevent failure. The objective of this study is to present a probabilistic risk analysis model to analyze the safety of this process. The standard risk assessment normally conducted is inadequate to address the risks. To remedy this problem, the model presented in this paper considers the effects of human, software and calibration reliability in the process. Bayesian Belief Network coupled to a Bow Tie diagram is used to identify potential engine failure scenarios. In this context and to meet this objective, an in depth literature research was conducted to identify the most appropriate modeling techniques and an interview were conducted with experts. As a result of this study, this paper presents a model that combines fault tree analysis, event tree analysis and a Bayesian Belief Networks into a single model that can be used by decision makers to identify critical risk factors in order to allocate resources to improve the safety of the system. The model is delivered in the form of a computer assisted decision tool supported by subject expert estimates.

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“…The estimations are closed-form solutions and simulations are generated for different levels of masking. Regarding population variability analysis, Droguett et al (2004) provided models based on the FB approach, which can use both observed data and expert judgment. They use a nonconjugate lognormal-Poisson-lognormal likelihood model that considers a continuous parameter space made possible by the MCMC method.…”

Section: Data Analysis and Assessment Of Reliability Indicesmentioning

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...

show abstract

The combined use of data and expert estimates in population variability analysis

Cited by 36 publications

References 14 publications

Bayesian assessment of the variability of reliability measures

Bayesian assessment of the variability of reliability measures

Probabilistic risk analysis in manufacturing situational operation: application of modelling techniques and causal structure to improve safety performance.

Risk and reliability assessment in chemical process industries using Bayesian methods

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