Agile Bayesian belief networks and application on complex system reliability growth analysis

1994 ◽

Vol 01 (01) ◽

pp. 71-83 ◽

Cited By ~ 5

Author(s):

M. XIE ◽

T.N. GOH

Keyword(s):

System Reliability ◽

Growth Analysis ◽

Simple Approach ◽

System Level ◽

Reliability Growth ◽

System Failure ◽

Component Level ◽

Component Failure ◽

Failure Data ◽

Growth Estimation

In this paper the problem of system-level reliability growth estimation using component-level failure data is studied. It is suggested that system failure data should be broken down into component, or subsystem, failure data when the above problems have occurred during the system testing phase. The proposed approach is especially useful when the system is not unchanged over the time, when some subsystems are improved more than others, or when the testing has been concentrated on different components at different time. These situations usually happen in practice and it may also be the case even if the system failure data is provided. Two sets of data are used to illustrate the simple approach; one is a set of component failure data for which all subsystems are available for testing at the same time and for the other set of data, the starting times are different for different subsystems.

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A Bayesian Model for Complex System Reliability Growth Under Arbitrary Corrective Actions

IEEE Transactions on Reliability ◽

10.1109/tr.2014.2337072 ◽

2015 ◽

Vol 64 (1) ◽

pp. 206-220 ◽

Cited By ~ 19

Author(s):

Martin Wayne ◽

Mohammad Modarres

Keyword(s):

Complex System ◽

System Reliability ◽

Bayesian Model ◽

Reliability Growth ◽

Corrective Actions

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Reliability growth analysis of k-out-of-N systems using matrix-based system reliability method

Reliability Engineering & System Safety ◽

10.1016/j.ress.2017.05.001 ◽

2017 ◽

Vol 165 ◽

pp. 410-421 ◽

Cited By ~ 18

Author(s):

Ji-Eun Byun ◽

Hee-Min Noh ◽

Junho Song

Keyword(s):

System Reliability ◽

Growth Analysis ◽

Reliability Growth ◽

Reliability Method

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System Reliability Growth Analysis during Warranty

International Journal of Mathematical Engineering and Management Sciences ◽

10.33889/ijmems.2019.4.1-008 ◽

2019 ◽

Vol 4 (1) ◽

pp. 85-94 ◽

Cited By ~ 1

Author(s):

James Li ◽

Greg Collins ◽

Ravi Govindarajulu

Keyword(s):

System Reliability ◽

Growth Analysis ◽

Growth Models ◽

Primary Objective ◽

Reliability Growth ◽

Failure Data ◽

Duane Model ◽

Actual Field ◽

Reliability Performance ◽

Field Failure

This paper presents system reliability growth analysis using actual field failure data. The primary objective of the system reliability growth is to improve the achievement of system reliability performance during system reliability demonstration, in order to achieve the predicted or contractually required system reliability commitment. An effective reliability growth model can be utilized to predict when the reliability target can be achieved based on previous reliability performance. In this paper, the system reliability growth analysis is illustrated using the Duane and AMSAA reliability growth models to determine applicability and aid in choice determination. The Duane model is a better choice for failure terminated reliability growth while AMSAA is a better choice for time terminated reliability growth. Comparisons of the Duane versus AMSAA model are carried out by conducting the statistical analysis on the observed field failures.

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Path Associative Networks and Bayesian Belief Networks A career decision support tool

PsycEXTRA Dataset ◽

10.1037/e683322011-113 ◽

2000 ◽

Author(s):

Clarence C. Rohrbaugh

Keyword(s):

Decision Support ◽

Decision Support Tool ◽

Career Decision ◽

Bayesian Belief Networks ◽

Belief Networks ◽

Associative Networks ◽

Support Tool

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Using Bayesian network models to incorporate uncertainty in the economic analysis of pollution abatement measures under the water framework directive

Water Science & Technology Water Supply ◽

10.2166/ws.2005.0054 ◽

2005 ◽

Vol 5 (6) ◽

pp. 95-104 ◽

Cited By ~ 4

Author(s):

D.N. Barton ◽

T. Saloranta ◽

T.H. Bakken ◽

A. Lyche Solheim ◽

J. Moe ◽

...

Keyword(s):

Water Quality ◽

At Risk ◽

Economic Valuation ◽

Pollution Abatement ◽

Network Models ◽

Cost Effective ◽

Bayesian Belief Networks ◽

Probabilistic Interpretation ◽

Belief Networks ◽

Benefit Cost Analysis

The evaluation of water bodies “at risk” of not achieving the Water Framework Directive's (WFD) goal of “good status” begs the question of how big a risk is acceptable before a programme of measures should be implemented. Documentation of expert judgement and statistical uncertainty in pollution budgets and water quality modelling, combined with Monte Carlo simulation and Bayesian belief networks, make it possible to give a probabilistic interpretation of “at risk”. Combined with information on abatement costs, a cost-effective ranking of measures based on expected costs and effect can be undertaken. Combined with economic valuation of water quality, the definition of “disproportionate cost” of abatement measures compared to benefits of achieving “good status” can also be given a probabilistic interpretation. Explicit modelling of uncertainty helps visualize where research and consulting efforts are most critical for reducing uncertainty. Based on data from the Morsa catchment in South-Eastern Norway, this paper discusses the relative merits of using Bayesian belief networks when integrating biophysical modelling results in the benefit-cost analysis of derogations and cost-effectiveness ranking of abatement measures under the WFD.

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Using Bayesian Belief Networks to assess the influence of landscape connectivity on ecosystem service trade-offs and synergies in urban landscapes in the UK

Landscape Ecology ◽

10.1007/s10980-021-01307-6 ◽

2021 ◽

Author(s):

James D. Karimi ◽

Jim A. Harris ◽

Ron Corstanje

Keyword(s):

Functional Connectivity ◽

Ecosystem Service ◽

Urban Areas ◽

Landscape Connectivity ◽

Structural Characteristics ◽

Bird Species ◽

Bayesian Belief Networks ◽

Belief Networks ◽

Patch Area ◽

Trade Offs

Abstract Context Landscape connectivity is assumed to influence ecosystem service (ES) trade-offs and synergies. However, empirical studies of the effect of landscape connectivity on ES trade-offs and synergies are limited, especially in urban areas where the interactions between patterns and processes are complex. Objectives The objectives of this study were to use a Bayesian Belief Network approach to (1) assess whether functional connectivity drives ES trade-offs and synergies in urban areas and (2) assess the influence of connectivity on the supply of ESs. Methods We used circuit theory to model urban bird flow of P. major and C. caeruleus at a 2 m spatial resolution in Bedford, Luton and Milton Keynes, UK, and Bayesian Belief Networks (BBNs) to assess the sensitivity of ES trade-offs and synergies model outputs to landscape and patch structural characteristics (patch area, connectivity and bird species abundance). Results We found that functional connectivity was the most influential variable in determining two of three ES trade-offs and synergies. Patch area and connectivity exerted a strong influence on ES trade-offs and synergies. Low patch area and low to moderately low connectivity were associated with high levels of ES trade-offs and synergies. Conclusions This study demonstrates that landscape connectivity is an influential determinant of ES trade-offs and synergies and supports the conviction that larger and better-connected habitat patches increase ES provision. A BBN approach is proposed as a feasible method of ES trade-off and synergy prediction in complex landscapes. Our findings can prove to be informative for urban ES management.

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