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Felipe Aguirre - One of the best experts on this subject based on the ideXlab platform.

  • Reliability assessment for multi-State systems under uncertainties based on the Dempster–Shafer theory
    Iie Transactions, 2013
    Co-Authors: Mohamed Sallak, Walter Schön, Felipe Aguirre
    Abstract:

    This article presents an original method for evaluating reliability indices for Multi-State Systems (MSSs) in the presence of aleatory and epistemic uncertainties. In many real- world MSSs, an insufficiency of data makes it difficult to estimate precise values for Component State probabilities. The proposed approach applies the transferable belief model interpretation of the Dempster–Shafer theory to represent Component State beliefs and to evaluate the MSS reliability indices. The example of an oil transmission system is used to demonstrate the proposed approach and it is compared with the universal generating function method. The value of the Dempster–Shafer theory lies in its ability to use several combination rules in order to evaluate reliability indices for MSSs that depend on the reliability of the experts’ opinions as well as their independence.

  • Reliability assessment for multi-State systems under uncertainties based on the Dempster-Shafer theory
    IIE Transactions, 2013
    Co-Authors: Mohamed Sallak, Walter Schön, Felipe Aguirre
    Abstract:

    This paper presents an original method for evaluating reliability indices for Multi-State Systems (MSSs) in the presence of aleatory and epistemic uncertainties. In many real world MSSs an insufficiency of data makes it difficult to estimate precise values for Component State probabilities. The proposed approach applies the Transferable Belief Model (TBM) interpretation of the Dempster-Shafer theory to represent Component State beliefs and to evaluate the MSS reliability indices. We use the example of an oil transmission system to demonstrate the proposed approach and we compare it with the Universal Generating Function method. The value of the Dempster-Shafer theory lies in its ability to use several combination rules in order to evaluate reliability indices for MSSs that depend on the reliability of the experts' opinions as well as their independence.

  • Extended Component importance measures considering aleatory and epistemic uncertainties
    IEEE Transactions on Reliability, 2013
    Co-Authors: Mohamed Sallak, Walter Schön, Felipe Aguirre
    Abstract:

    In this paper, extended Component importance measures (Birnbaum importance, RAW, RRW and Crit- icality importance) considering aleatory and epistemic uncertainties are introduced. The D-S theory which is considered to be a less restricted extension of probability theory is proposed as a framework for taking into account both aleatory and epistemic uncertainties. The epistemic uncertainty defined in this paper is the total lack of knowledge of the Component State. The objective is to translate this epistemic uncertainty to the epistemic uncertainty of system State and to the epistemic uncertainty of importance measures of Components. The Affine Arithmetic allows us to provide much tighter bounds in the computing process of interval bounds of importance measures avoiding the error explosion problem. The efficiency of the proposed measures is demonstrated using a bridge system with different types of reliability data (aleatory uncertainty, epistemic uncertainty and experts' judgments). The influence of the epistemic uncertainty on the Components' rankings is described. Finally, a case study of a fire-detector system located in a production room is provided. A comparison between the proposed measures and the probabilistic importance measures using two-stage Monte Carlo simulations is also made.

Mohamed Sallak - One of the best experts on this subject based on the ideXlab platform.

  • Reliability assessment for multi-State systems under uncertainties based on the Dempster–Shafer theory
    Iie Transactions, 2013
    Co-Authors: Mohamed Sallak, Walter Schön, Felipe Aguirre
    Abstract:

    This article presents an original method for evaluating reliability indices for Multi-State Systems (MSSs) in the presence of aleatory and epistemic uncertainties. In many real- world MSSs, an insufficiency of data makes it difficult to estimate precise values for Component State probabilities. The proposed approach applies the transferable belief model interpretation of the Dempster–Shafer theory to represent Component State beliefs and to evaluate the MSS reliability indices. The example of an oil transmission system is used to demonstrate the proposed approach and it is compared with the universal generating function method. The value of the Dempster–Shafer theory lies in its ability to use several combination rules in order to evaluate reliability indices for MSSs that depend on the reliability of the experts’ opinions as well as their independence.

  • Reliability assessment for multi-State systems under uncertainties based on the Dempster-Shafer theory
    IIE Transactions, 2013
    Co-Authors: Mohamed Sallak, Walter Schön, Felipe Aguirre
    Abstract:

    This paper presents an original method for evaluating reliability indices for Multi-State Systems (MSSs) in the presence of aleatory and epistemic uncertainties. In many real world MSSs an insufficiency of data makes it difficult to estimate precise values for Component State probabilities. The proposed approach applies the Transferable Belief Model (TBM) interpretation of the Dempster-Shafer theory to represent Component State beliefs and to evaluate the MSS reliability indices. We use the example of an oil transmission system to demonstrate the proposed approach and we compare it with the Universal Generating Function method. The value of the Dempster-Shafer theory lies in its ability to use several combination rules in order to evaluate reliability indices for MSSs that depend on the reliability of the experts' opinions as well as their independence.

  • Extended Component importance measures considering aleatory and epistemic uncertainties
    IEEE Transactions on Reliability, 2013
    Co-Authors: Mohamed Sallak, Walter Schön, Felipe Aguirre
    Abstract:

    In this paper, extended Component importance measures (Birnbaum importance, RAW, RRW and Crit- icality importance) considering aleatory and epistemic uncertainties are introduced. The D-S theory which is considered to be a less restricted extension of probability theory is proposed as a framework for taking into account both aleatory and epistemic uncertainties. The epistemic uncertainty defined in this paper is the total lack of knowledge of the Component State. The objective is to translate this epistemic uncertainty to the epistemic uncertainty of system State and to the epistemic uncertainty of importance measures of Components. The Affine Arithmetic allows us to provide much tighter bounds in the computing process of interval bounds of importance measures avoiding the error explosion problem. The efficiency of the proposed measures is demonstrated using a bridge system with different types of reliability data (aleatory uncertainty, epistemic uncertainty and experts' judgments). The influence of the epistemic uncertainty on the Components' rankings is described. Finally, a case study of a fire-detector system located in a production room is provided. A comparison between the proposed measures and the probabilistic importance measures using two-stage Monte Carlo simulations is also made.

Enrico Zio - One of the best experts on this subject based on the ideXlab platform.

  • On the optimal redundancy allocation for multi-State series–parallel systems under epistemic uncertainty
    Reliability Engineering and System Safety, 2017
    Co-Authors: Mu-xia Sun, Enrico Zio
    Abstract:

    In this paper, we study the redundancy allocation problem (RAP) for multi-State series–parallel systems (MSSPSs). For each multi-State Component, the exact values of its State probabilities are assumed to be unknown, due to epistemic uncertainty (EU), and only conservative lower and upper bounds of them are given. The objective of the RAP is to simultaneously maximize the supremum and infimum of the system's uncertain availability, under a cost constraint. The problem is two-stage and multi-objective. In this work, we: 1. provide a linear-time algorithm to obtain the Component State distribution, under which the uncertain system availability will be at its supremum or infimum; 2. show that the problem is reducible to one-stage; 3. analyze the landscape of MSSPS RAP under EU and propose a modified NSGA-II, with targeted designs of repair and local search operation. The proposed algorithm is compared with standard NSGA-II on multiple benchmarks. The results show that the proposed algorithm significantly outperforms the standard NSGA-II in both optimality and time efficiency.

  • Integrating Random Shocks Into Multi-State Physics Models of Degradation Processes for Component Reliability Assessment
    IEEE Transactions on Reliability, 2015
    Co-Authors: Yan-hui Lin, Enrico Zio
    Abstract:

    We extend a multi-State physics model (MSPM) framework for Component reliability assessment by including semi-Markov and random shock processes. Two mutually exclusive types of random shocks are considered: extreme, and cumulative. Extreme shocks lead the Component to immediate failure, whereas cumulative shocks simply affect the Component degradation rates. General dependences between the degradation and the two types of random shocks are considered. A Monte Carlo simulation algorithm is implemented to compute Component State probabilities. An illustrative example is presented, and a sensitivity analysis is conducted on the model parameters. The results show that our extended model is able to characterize the influences of different types of random shocks onto the Component State probabilities and the reliability estimates.

Mu-xia Sun - One of the best experts on this subject based on the ideXlab platform.

  • On the optimal redundancy allocation for multi-State series–parallel systems under epistemic uncertainty
    Reliability Engineering and System Safety, 2017
    Co-Authors: Mu-xia Sun, Enrico Zio
    Abstract:

    In this paper, we study the redundancy allocation problem (RAP) for multi-State series–parallel systems (MSSPSs). For each multi-State Component, the exact values of its State probabilities are assumed to be unknown, due to epistemic uncertainty (EU), and only conservative lower and upper bounds of them are given. The objective of the RAP is to simultaneously maximize the supremum and infimum of the system's uncertain availability, under a cost constraint. The problem is two-stage and multi-objective. In this work, we: 1. provide a linear-time algorithm to obtain the Component State distribution, under which the uncertain system availability will be at its supremum or infimum; 2. show that the problem is reducible to one-stage; 3. analyze the landscape of MSSPS RAP under EU and propose a modified NSGA-II, with targeted designs of repair and local search operation. The proposed algorithm is compared with standard NSGA-II on multiple benchmarks. The results show that the proposed algorithm significantly outperforms the standard NSGA-II in both optimality and time efficiency.

Walter Schön - One of the best experts on this subject based on the ideXlab platform.

  • Reliability assessment for multi-State systems under uncertainties based on the Dempster–Shafer theory
    Iie Transactions, 2013
    Co-Authors: Mohamed Sallak, Walter Schön, Felipe Aguirre
    Abstract:

    This article presents an original method for evaluating reliability indices for Multi-State Systems (MSSs) in the presence of aleatory and epistemic uncertainties. In many real- world MSSs, an insufficiency of data makes it difficult to estimate precise values for Component State probabilities. The proposed approach applies the transferable belief model interpretation of the Dempster–Shafer theory to represent Component State beliefs and to evaluate the MSS reliability indices. The example of an oil transmission system is used to demonstrate the proposed approach and it is compared with the universal generating function method. The value of the Dempster–Shafer theory lies in its ability to use several combination rules in order to evaluate reliability indices for MSSs that depend on the reliability of the experts’ opinions as well as their independence.

  • Reliability assessment for multi-State systems under uncertainties based on the Dempster-Shafer theory
    IIE Transactions, 2013
    Co-Authors: Mohamed Sallak, Walter Schön, Felipe Aguirre
    Abstract:

    This paper presents an original method for evaluating reliability indices for Multi-State Systems (MSSs) in the presence of aleatory and epistemic uncertainties. In many real world MSSs an insufficiency of data makes it difficult to estimate precise values for Component State probabilities. The proposed approach applies the Transferable Belief Model (TBM) interpretation of the Dempster-Shafer theory to represent Component State beliefs and to evaluate the MSS reliability indices. We use the example of an oil transmission system to demonstrate the proposed approach and we compare it with the Universal Generating Function method. The value of the Dempster-Shafer theory lies in its ability to use several combination rules in order to evaluate reliability indices for MSSs that depend on the reliability of the experts' opinions as well as their independence.

  • Extended Component importance measures considering aleatory and epistemic uncertainties
    IEEE Transactions on Reliability, 2013
    Co-Authors: Mohamed Sallak, Walter Schön, Felipe Aguirre
    Abstract:

    In this paper, extended Component importance measures (Birnbaum importance, RAW, RRW and Crit- icality importance) considering aleatory and epistemic uncertainties are introduced. The D-S theory which is considered to be a less restricted extension of probability theory is proposed as a framework for taking into account both aleatory and epistemic uncertainties. The epistemic uncertainty defined in this paper is the total lack of knowledge of the Component State. The objective is to translate this epistemic uncertainty to the epistemic uncertainty of system State and to the epistemic uncertainty of importance measures of Components. The Affine Arithmetic allows us to provide much tighter bounds in the computing process of interval bounds of importance measures avoiding the error explosion problem. The efficiency of the proposed measures is demonstrated using a bridge system with different types of reliability data (aleatory uncertainty, epistemic uncertainty and experts' judgments). The influence of the epistemic uncertainty on the Components' rankings is described. Finally, a case study of a fire-detector system located in a production room is provided. A comparison between the proposed measures and the probabilistic importance measures using two-stage Monte Carlo simulations is also made.