Bayesian assessment of the variability of reliability measures

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

doi:10.1590/s0101-74382006000100006

Cited by 7 publications

(3 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Such evidences might have different natures and come from various sources. [45][46][47] The SHM framework can benefit from useful information derived from online and offline monitoring data (collected from built-in sensors and NDI, respectively), partially relevant data (comes from similar systems but not necessarily identical to the target system), expert opinion, any other sources such as relevant published literature, reliability handbooks, and historical data.…”

Section: Dbn Approach To Integrate Various Evidencesmentioning

confidence: 99%

A prognostics approach based on the evolution of damage precursors using dynamic Bayesian networks

Rabiei

Droguett

Modarres

2016

Advances in Mechanical Engineering

View full text Add to dashboard Cite

During the lifetime of a component, microstructural changes emerge at its material level and evolve through time. Classical empirical degradation models (e.g. Paris Law in fatigue crack growth) are usually established based on monitoring and estimating well-known direct damage indicators such as crack size. However, by the time the usual inspection techniques efficiently identify such damage indicators, most of the life of the component would have been expended, and usually it would be too late to save the component. Therefore, it is important to detect damage at the earliest possible time. This article presents a new structural health monitoring and damage prognostics framework based on evolution of damage precursors representing the indirect damage indicators, when conventional direct damage indicator, such as a crack, is unobservable, inaccessible, or difficult to measure. Dynamic Bayesian network is employed to represent all the related variables as well as their causal or correlation relationships. Since the degradation model based on damage precursor evolution is not fully recognized, the methodology needs to be capable of online-learning the degradation process as well as estimating the damage state. Therefore, the joint particle filtering technique is implemented as an inference method inside the dynamic Bayesian network to assess both model parameters and damage states simultaneously. The proposed framework allows the integration of any related sources of information in order to reduce the inherent uncertainties. Incorporating different types of evidences in dynamic Bayesian network entails advance techniques to identify and formulate the possible interaction between potentially nonhomogenous variables. This article uses the support vector regression in order to define generally unknown nonparametric and nonlinear correlation between the input variables. The methodology is successfully applied to damage estimation and prediction of crack initiation in a metallic alloy under fatigue. The proposed framework is intended to be general and comprehensive so that it can be implemented in different applications.Chilean National Fund for Scientific and Technological Development (Fondecyt) 116049

show abstract

Section: Dbn Approach To Integrate Various Evidencesmentioning

confidence: 99%

A prognostics approach based on the evolution of damage precursors using dynamic Bayesian networks

Rabiei

Droguett

Modarres

2016

Advances in Mechanical Engineering

View full text Add to dashboard Cite

show abstract

“…A Bayesian hierarchical model is then used as framework for the HEP quantification from simulator data. Bayesian hierarchical models have been widely adopted in probabilistic safety assessment to treat source-tosource variability, [29][30][31][32][33][34][35][36] as well as in many other applications for inference of population-level quantities from group-level evidence and vice versa. [37][38][39][40][41][42] The paper is structured as follows.…”

Section: Introductionmentioning

confidence: 99%

Crew performance variability in human error probability quantification: A methodology based on behavioral patterns from simulator data

Greco

Podofillini

Dang

2021

Proceedings of the Institution of Mechanical Engineers, Part O:

View full text Add to dashboard Cite

Current Human Reliability Analysis models express error probabilities as a function of task types and operational context, without explicitly modelling the influence of different crew behavioral characteristics on the error probability. The influence of such variability is treated only implicitly, by variability and uncertainty distributions with bounds primarily obtained by expert judgment. This paper presents a methodology to empirically incorporate crew performance variability in error probability quantification, from simulator data. Crew behaviors are represented by a set of “behavioral patterns” that emerge in the observation of operating crews (e.g. in information sharing or in adhering to procedural guidance). The paper demonstrates the use of a Bayesian hierarchical model to explicitly capture the performance variability emerging from data. The methodology is applied to a case study from literature. Numerical demonstrations are performed in order to compare the proposed approach to the existing quantification models used in HRA for treating simulator data.

show abstract

“…A determinação desta função densidade de probabilidade torna-se fundamental dentro da análise, uma vez que a taxa de falha definida a partir desta função vai reger a transição entre os estados e, portanto, a probabilidade de um sistema estar em um estado específico de operação, dadas as entradas e saídas do sistema (DROGUETT, GROEN e MOSLEH, 2006;PALACIOS, et al, 2009).…”

Section: Análise De Mudança De Estadounclassified

Análise de risco em operações de 'offloading' - um modelo de avaliação probabilística dinâmica para a tomada de decisão.

Rodriguez¹,

Elena²

View full text Add to dashboard Cite

To explore offshore oil fields in deep water the use of a Floating Production Storage and Offloading (FPSO) unit coupled to a shuttle tanker is economically and technically feasible. The FPSO unit normally consists of a ship shaped hull, with an internal or external turret, and production equipment on the deck. The unit is also equipped for crude oil storage. Oil transportation systems required for supporting this infrastructure are pipelines or shuttle tankers. Shuttle tankers are increasingly being accepted as a preferred transportation method for remote and deepwater offshore developments. The offloading operation is considered one of the most risky operations in offshore environments. This dissertation presents a risk-based analysis method aiming at defining the risk profile associated with an offloading operation. For offloading operations the risk profile is usually

show abstract

Bayesian assessment of the variability of reliability measures

Cited by 7 publications

References 13 publications

A prognostics approach based on the evolution of damage precursors using dynamic Bayesian networks

A prognostics approach based on the evolution of damage precursors using dynamic Bayesian networks

Crew performance variability in human error probability quantification: A methodology based on behavioral patterns from simulator data

Análise de risco em operações de 'offloading' - um modelo de avaliação probabilística dinâmica para a tomada de decisão.

Contact Info

Product

Resources

About