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Rong Pan - One of the best experts on this subject based on the ideXlab platform.

  • System Reliability Assessment Through Bayesian Network Modeling
    Advances in System Reliability Engineering, 2019
    Co-Authors: Rong Pan, Petek Yontay, Dongjin Lee, Luis Mejia Sanchez
    Abstract:

    Abstract Using a Bayesian network (BN) model to evaluate system Reliability is presented in this chapter. The BN model is a graphical model that generalizes the deterministic system Reliability Structure typically represented by a Reliability block diagram or a fault tree, because BN allows inclusion of the uncertainty in system Structure to be in the model. The information fusion for establishing BN models is discussed. We use an example to illustrate how to utilize this modeling technique to make design decisions for a complex system.

  • A nonparametric Bayesian network approach to assessing system Reliability at early design stages
    Reliability Engineering & System Safety, 2018
    Co-Authors: Dongjin Lee, Rong Pan
    Abstract:

    Abstract It is important to predict a system’s Reliability at its early design stages because modifying design to improve Reliability and maintainability at a later time in the system’s lifecycle will be costly and, oftentimes, impossible. However, this early prediction is challenging because of the lack of Reliability data and the incomplete knowledge of a complex system’s Reliability Structure. To tackle this problem, this paper presents a nonparametric Bayesian network approach. Employing nonparametric Bayesian network, the limitation of discrete Bayesian network can be overcome, and it can be used as a useful tool for decision support. The proposed methodology is applied to a case study to demonstrate its prognostic and diagnostic capabilities.

  • Predictive maintenance of complex system with multi-level Reliability Structure
    International Journal of Production Research, 2017
    Co-Authors: Dongjin Lee, Rong Pan
    Abstract:

    Onboard sensors, which constantly monitor the states of a system and its components, have made the predictive maintenance (PdM) of a complex system possible. To date, system Reliability has been extensively studied with the assumption that systems are either single-component systems or they have a deterministic Reliability Structure. However, in many realistic problems, there are complex multi-component systems with uncertainties in the system Reliability Structure. This paper presents a PdM scheme for complex systems by employing discrete time Markov chain models for modelling multiple degradation processes of components and a Bayesian network (BN) model for predicting system Reliability. The proposed method can be considered as a special type of dynamic Bayesian network because the same BN is repeatedly used over time for evaluating system Reliability and the inter-time–slice connection of the same node is monitored by a sensor. This PdM scheme is able to make probabilistic inference at any system level...

  • A computational Bayesian approach to dependency assessment in system Reliability
    Reliability Engineering and System Safety, 2016
    Co-Authors: Petek Yontay, Rong Pan
    Abstract:

    Due to the increasing complexity of engineered products, it is of great importance to develop a tool to assess Reliability dependencies among components and systems under the uncertainty of system Reliability Structure. In this paper, a Bayesian network approach is proposed for evaluating the conditional probability of failure within a complex system, using a multilevel system configuration. Coupling with Bayesian inference, the posterior distributions of these conditional probabilities can be estimated by combining failure information and expert opinions at both system and component levels. Three data scenarios are considered in this study, and they demonstrate that, with the quantification of the stochastic relationship of Reliability within a system, the dependency Structure in system Reliability can be gradually revealed by the data collected at different system levels.

Markos V. Koutras - One of the best experts on this subject based on the ideXlab platform.

  • Reliability Analysis of Coherent Systems with Exchangeable Components
    Applications of Mathematics and Informatics in Science and Engineering, 2014
    Co-Authors: Markos V. Koutras, Ioannis S. Triantafyllou
    Abstract:

    In this paper we study Reliability properties of coherent systems consisting of n exchangeable components. We focus on the aging behavior of a Reliability Structure and several results are reached clarifying whether a system displays the IFR/DFR property or not. More specifically, a necessary and sufficient condition is deduced for a system’s lifetime to be IFR, while additional signature-based conditions aiming at the same direction are also delivered. For illustration purposes, special cases of well-known Reliability systems and specific lifetimes’ distributions are considered and studied in detail.

  • On the signature of coherent systems and applications
    Probability in the Engineering and Informational Sciences, 2007
    Co-Authors: Ioannis S. Triantafyllou, Markos V. Koutras
    Abstract:

    In the present article we provide a formula that facilitates the evaluation of the signature of a Reliability Structure by a generating function approach. A simple sufficient condition is also derived for proving the nonpreservation of the IFR property for the system's lifetime (when the components are IFR) by exploiting the signature of the system. As an application of the general results, we deduce recurrence relations for the signature of a linear consecutive k-out-of-n: F system. We establish a simple relation between the signature of a linear and a circular system and investigate the IFR preservation property under the formulation of such systems.

  • BOUNDS FOR THE DISTRIBUTION OF TWO-DIMENSIONAL BINARY SCAN STATISTICS
    Probability in the Engineering and Informational Sciences, 2003
    Co-Authors: Michael V. Boutsikas, Markos V. Koutras
    Abstract:

    In the present article, we develop some efficient bounds for the distribution function of a two-dimensional scan statistic defined on a (double) sequence of independent and identically distributed (i.i.d.) binary trials. The methodology employed here takes advantage of the connection between the scan statistic problem and an equivalent Reliability Structure and exploits appropriate techniques of Reliability theory to establish tractable bounds for the distribution of the statistic of interest. An asymptotic result is established and a numerical study is carried out to investigate the efficiency of the suggested bounds.

  • Generalized Reliability bounds for coherent Structures
    Journal of Applied Probability, 2000
    Co-Authors: Michael V. Boutsikas, Markos V. Koutras
    Abstract:

    In this article we introduce generalizations of several well-known Reliability bounds. These bounds are based on arbitrary partitions of the family of minimal path or cut sets of the system and can be used for approximating the Reliability of any coherent Structure with i.i.d. components. An illustration is also given of how the general results can be applied for a specific Reliability Structure (two-dimensional consecutive- k 1 x k 2 -out-of- n 1 x n 2 system) along with extensive numerical calculations revealing that, in most cases, the generalized bounds perform better than other available bounds in the literature for this system.

Dongjin Lee - One of the best experts on this subject based on the ideXlab platform.

  • System Reliability Assessment Through Bayesian Network Modeling
    Advances in System Reliability Engineering, 2019
    Co-Authors: Rong Pan, Petek Yontay, Dongjin Lee, Luis Mejia Sanchez
    Abstract:

    Abstract Using a Bayesian network (BN) model to evaluate system Reliability is presented in this chapter. The BN model is a graphical model that generalizes the deterministic system Reliability Structure typically represented by a Reliability block diagram or a fault tree, because BN allows inclusion of the uncertainty in system Structure to be in the model. The information fusion for establishing BN models is discussed. We use an example to illustrate how to utilize this modeling technique to make design decisions for a complex system.

  • A nonparametric Bayesian network approach to assessing system Reliability at early design stages
    Reliability Engineering & System Safety, 2018
    Co-Authors: Dongjin Lee, Rong Pan
    Abstract:

    Abstract It is important to predict a system’s Reliability at its early design stages because modifying design to improve Reliability and maintainability at a later time in the system’s lifecycle will be costly and, oftentimes, impossible. However, this early prediction is challenging because of the lack of Reliability data and the incomplete knowledge of a complex system’s Reliability Structure. To tackle this problem, this paper presents a nonparametric Bayesian network approach. Employing nonparametric Bayesian network, the limitation of discrete Bayesian network can be overcome, and it can be used as a useful tool for decision support. The proposed methodology is applied to a case study to demonstrate its prognostic and diagnostic capabilities.

  • Predictive maintenance of complex system with multi-level Reliability Structure
    International Journal of Production Research, 2017
    Co-Authors: Dongjin Lee, Rong Pan
    Abstract:

    Onboard sensors, which constantly monitor the states of a system and its components, have made the predictive maintenance (PdM) of a complex system possible. To date, system Reliability has been extensively studied with the assumption that systems are either single-component systems or they have a deterministic Reliability Structure. However, in many realistic problems, there are complex multi-component systems with uncertainties in the system Reliability Structure. This paper presents a PdM scheme for complex systems by employing discrete time Markov chain models for modelling multiple degradation processes of components and a Bayesian network (BN) model for predicting system Reliability. The proposed method can be considered as a special type of dynamic Bayesian network because the same BN is repeatedly used over time for evaluating system Reliability and the inter-time–slice connection of the same node is monitored by a sensor. This PdM scheme is able to make probabilistic inference at any system level...

Vitaly Levashenko - One of the best experts on this subject based on the ideXlab platform.

  • Construction of a Reliability Structure Function Based on Uncertain Data
    IEEE Transactions on Reliability, 2016
    Co-Authors: Elena Zaitseva, Vitaly Levashenko
    Abstract:

    Structure function is one of the possible mathematical models of the real systems under study in Reliability engineering. The Structure function represents the correlation the system performance level and components states. The system performance level is defined from the states of all its components. It means that all possible components states and performance levels must be indicated and reflected in the Structure function. Initial data for the analysis of real system is uncertain. New methods to construct a Structure function based on initial uncertain data is proposed. Fuzzy decision trees (FDTs) are used in this method to transform initial uncertain data about a real system into an exact-defined a system Structure function. The proposed method includes three principal steps for the Structure function construction: 1) collection of data in the repository; 2) representation of the system model in the form of an FDT; and 3) construction of the Structure function based on the FDT.

Sean Reed - One of the best experts on this subject based on the ideXlab platform.

  • An efficient algorithm for exact computation of system and survival signatures using binary decision diagrams
    Reliability Engineering & System Safety, 2017
    Co-Authors: Sean Reed
    Abstract:

    System and survival signatures are important and popular tools for studying and analysing the Reliability of systems. However, it is difficult to compute these signatures for systems with complex Reliability Structure functions and large numbers of components. This paper presents a new algorithm that is able to compute exact signatures for systems that are far more complex than is feasible using existing approaches. This is based on the use of reduced order binary decision diagrams (ROBDDs), multidimensional arrays and the dynamic programming paradigm. Results comparing the computational efficiency of deriving signatures for some example systems (including complex benchmark systems from the literature) using the new algorithm and a comparison enumerative algorithm are presented and demonstrate a significant reduction in computation time and improvement in scalability with increasing system complexity.

  • Improved Efficiency in the Analysis of Phased Mission Systems With Multiple Failure Mode Components
    IEEE Transactions on Reliability, 2011
    Co-Authors: Sean Reed, John Andrews, Sarah J. Dunnett
    Abstract:

    Systems often operate in phased missions where their Reliability Structure varies over a set of consecutive time periods, known as phases. The Reliability of a phased mission is defined as the probability that all phases in the mission are completed without failure. While the Binary Decision Diagram (BDD) method has been shown to be the most efficient solution for measuring the Reliability of phased missions with non-repairable components with mutually exclusive failure modes, the existing BDD based methods are still unable to analyze large systems without considerable computational expense. This paper introduces a new BDD based method that is shown to provide improved efficiency and accuracy in the repeat analysis of this type of phased mission.