Showing papers in "Reliability Engineering & System Safety in 2006"

TL;DR: An overview and tutorial is presented describing genetic algorithms (GA) developed specifically for problems with multiple objectives that differ primarily from traditional GA by using specialized fitness functions and introducing methods to promote solution diversity.

TL;DR: Sampling-based methods for uncertainty and sensitivity analysis are reviewed and special attention is given to the determination of sensitivity analysis results.

TL;DR: This tutorial is to introduce the more general reader to the Bayesian approach to quantifying, analysing and reducing uncertainty in the application of complex process models.

TL;DR: It is demonstrated that this use of OAT methods is illicit and unjustified, unless the model under analysis is proved to be linear, and it is shown that available good practices are able to overcome OAT shortcomings and easy to implement.

TL;DR: A comprehensive treatment of the mathematical properties of the beta exponential distribution generated from the logit of a beta random variable is provided and an expression for the Fisher information matrix is provided.

TL;DR: Results have shown that DEMATEL method can be an efficient, complementary and confident approach for reprioritization of failure modes in a FMEA and can cover some of inherently shortcomings of conventional Risk Priority Number method and like.

TL;DR: Some technical challenges that must be resolved for successful validation of a predictive modeling capability are identified and a formal description of a “model discrepancy” term is identified.

TL;DR: The methods adopt Satterthwaite's application of random balance designs in regression problems, and extend it to sensitivity analysis of model output for non-linear, non-additive models to reduce significantly the computational cost of the analysis.

TL;DR: A methodology that will help developing Dynamic Object Oriented Bayesian Networks (DOOBNs) to formalise such complex dynamic models, and has been tested, in an industrial context, to model the reliability of a water (immersion) heater system.

TL;DR: A ‘Lexicographic’ Goal Programming (LGP) approach to define the best strategies for the maintenance of critical centrifugal pumps in an oil refinery using the classic parameters occurrence, severity and detectability.

TL;DR: This paper summarizes several alternate views of ‘emergence’ and aims to lend greater clarity and reduce confusion whenever this term is applied to the engineering of complex systems.

TL;DR: This paper is using a fuzzy classification system for human reliability analysis in order to calculate the probability of erroneous actions according to CREAM in specific contexts e.g. maintenance tasks, in-field actions or control room operations in the running of a chemical plant.

TL;DR: A Genetic Algorithm (GA), as a soft computing approach, is a powerful tool for solving various reliability optimization problems, such as reliability optimization of redundant system, reliability optimized with alternative design, reliability optimization with time-dependent reliability, reliability Optimization with interval coefficients, bicriteria reliability optimization, and reliability optimize with fuzzy goals.

TL;DR: A new application of the multidimensional generalization of classical sensitivity indices, resulting from group sensitivities (sensitivity of the output of the model to a group of inputs), and an estimation method based on Monte-Carlo simulations are proposed.

TL;DR: A novel methodology for preventive maintenance policy evaluation based upon a cost-reliability model, which allows the use of flexible intervals between maintenance interventions is presented, allowing specific analysis on the weighting factors of the objective function.

TL;DR: A Bayesian methodology for assessing the confidence in model prediction by comparing the model output with experimental data when both are stochastic is developed.

TL;DR: Methods for characterizing a set of functions generated by a series of model runs for the purpose of exploring relationships between these functions and the model inputs are explored.

TL;DR: The MOEA used in this paper (NSGA-II) demonstrates the ability to identify a set of optimal solutions (Pareto front), which provides the Decision Maker with a complete picture of the optimal solution space.

TL;DR: A number of recent applications are presented in which an emulator of a computer code is created using a Gaussian process model, then tools are applied to the emulator to perform sensitivity analysis and uncertainty analysis.

TL;DR: A model is developed to calculate the risks and costs associated with an inspection strategy, giving credit to the realistic issues of the rail failure process and including the actual inspection and maintenance procedures followed by the railway company.

TL;DR: By using Markov model, the system state transition process can be clearly illustrated, and furthermore, the solutions of system availability and reliability are obtained based on this.

TL;DR: The relationship between PBA and the methods of interval analysis and probabilistic uncertainty analysis are shown, and how the method can be used to assess the quality of probabilism models such as those developed in Monte Carlo simulations for risk analyses are indicated.

TL;DR: The metaheuristics are based on artificial reasoning rather than on classical mathematical programming and do not require any information about the objective function besides its values corresponding to the points visited in the solution space.

TL;DR: In this paper, an evaluation system is proposed that carries out the decision making in relation to the feasibility of the setting up of a Predictive Maintenance Program using a combination of tools belonging to operational research such as: Analytic Hierarchy Process, decision rules and Bayesian tools.

TL;DR: Two methods of global sensitivity analysis are applied to a complex crop model for estimating the sensitivity indices associated to 13 genetic parameters and it is shown that only five genetic parameters have a significant effect on crop yield and grain quality.

TL;DR: Three applications of sampling-based sensitivity analysis in conjunction with evidence theory representations for epistemic uncertainty in model inputs are described.

TL;DR: A periodic preventive maintenance policy is looked at which achieves a tradeoff between the penalty and maintenance costs.

TL;DR: The performance of all these sampling methods and a new variant (“Latinized” CVT) are further compared for non -uniform input distributions, given uncorrelated normal inputs in a 2-D test problem.

TL;DR: A composite measure of reliability, availability and maintainability parameters has been proposed for measuring the system performance and the insights obtained are useful in formulating strategies for optimal operation of the system.