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Binary Decision Diagrams and Extensions for System Reliability Analysis von Xing, Liudong (eBook)

  • Erscheinungsdatum: 05.06.2015
  • Verlag: Wiley-Scrivener
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Binary Decision Diagrams and Extensions for System Reliability Analysis

Preface Recent advances in science and technology have made modern engineering systems more powerful and sophisticated than ever. This decade particularly has witnessed several disruptive technological innovations in distributed and cloud computing, wireless sensor networks, internet of things, big data analytics, autonomous vehicles, space exploration that has pushed the limits of internet and mobile computing technologies beyond our imagination. The increasing level of sophistication and automation in engineering systems not only increases the complexity of these systems, but also increases the dependencies among components within these systems, and as a result, reliability analysis of these systems becomes more challenging than ever. At the same time, an accurate reliability modeling and analysis is crucial to verify whether a system has met desired reliability and availability requirements, as well as to determine optimal cost-effective design policies that maximize system reliability and/or performance. Reliability of a system depends on reliabilities of its components and the system design configuration that includes the assembly of its components. In general, both the system and its components can have multiple failure modes and performance levels, and they can operate at different environments, stress and demand levels at different phases during their entire mission or life time. As a result, the component failure behavior and system configuration can vary with phases. In most applications, the relationship between a system and its components can be represented using combinatorial models where the system state can be represented using a logic function of its components states. This function that maps the set of component states to the system state is known as a system structure function, which is dependent on the system configuration. Once the system structure function and reliabilities of the system components are determined, traditionally the system reliability was determined using truth-tables, pathsets/cut-sets based on inclusion-exclusion expansion or sum-of-disjoint products representation of the structure function. However, all these traditional reliability evaluation methods are computationally inefficient and are limited to small scale models or problems. To solve large models, bounding and approximating methods have been used. However, finding good bounds and approximations were still considered as a challenging problem for several decades. This situation has changed after the seminal work by Bryant on binary decision diagrams (BDD) in 1986. BDD is the state-of-the-art data structure, which is primarily based on Shannon's decomposition theorem, used to encode and manipulate Boolean functions. The full potential for efficient algorithms based on the data structure of BDD is realized by Bryant's seminal work in 1986. Since then, BDD and its extended formats have been extensively applied in several fields including formal circuit verification and symbolic model checking. The success of BDD in these areas and the important applications of Boolean functions in system reliability analysis have stimulated considerable efforts to adapt BDD and its extended formats to reliability analysis of complex systems since 1993. These efforts have been firstly expended in reliability analysis of binary-state single-phase systems in which both the system and components exhibit only two states: operational or failed and their behaviors do not change throughout the mission. Many studies showed that in most cases, the BDD-based method requires less memory and computational time than other reliability analysis methods. Subsequently, various forms of decision diagrams have become the state-of-the-art combinatorial models for efficient reliability analysis of a wide range of complex systems, such as phased-mission systems, multi-state systems, fault-tolerant systems with imperfect fault coverage, s

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    Format: ePUB
    Kopierschutz: AdobeDRM
    Seitenzahl: 240
    Erscheinungsdatum: 05.06.2015
    Sprache: Englisch
    ISBN: 9781119178002
    Verlag: Wiley-Scrivener
    Größe: 6854 kBytes
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Binary Decision Diagrams and Extensions for System Reliability Analysis

Preface

Recent advances in science and technology have made modern engineering systems more powerful and sophisticated than ever. This decade particularly has witnessed several disruptive technological innovations in distributed and cloud computing, wireless sensor networks, internet of things, big data analytics, autonomous vehicles, space exploration that has pushed the limits of internet and mobile computing technologies beyond our imagination. The increasing level of sophistication and automation in engineering systems not only increases the complexity of these systems, but also increases the dependencies among components within these systems, and as a result, reliability analysis of these systems becomes more challenging than ever. At the same time, an accurate reliability modeling and analysis is crucial to verify whether a system has met desired reliability and availability requirements, as well as to determine optimal cost-effective design policies that maximize system reliability and/or performance.

Reliability of a system depends on reliabilities of its components and the system design configuration that includes the assembly of its components. In general, both the system and its components can have multiple failure modes and performance levels, and they can operate at different environments, stress and demand levels at different phases during their entire mission or life time. As a result, the component failure behavior and system configuration can vary with phases. In most applications, the relationship between a system and its components can be represented using combinatorial models where the system state can be represented using a logic function of its components states. This function that maps the set of component states to the system state is known as a system structure function, which is dependent on the system configuration. Once the system structure function and reliabilities of the system components are determined, traditionally the system reliability was determined using truth-tables, pathsets/cut-sets based on inclusion-exclusion expansion or sum-of-disjoint products representation of the structure function. However, all these traditional reliability evaluation methods are computationally inefficient and are limited to small scale models or problems. To solve large models, bounding and approximating methods have been used. However, finding good bounds and approximations were still considered as a challenging problem for several decades. This situation has changed after the seminal work by Bryant on binary decision diagrams (BDD) in 1986.

BDD is the state-of-the-art data structure, which is primarily based on Shannon's decomposition theorem, used to encode and manipulate Boolean functions. The full potential for efficient algorithms based on the data structure of BDD is realized by Bryant's seminal work in 1986. Since then, BDD and its extended formats have been extensively applied in several fields including formal circuit verification and symbolic model checking. The success of BDD in these areas and the important applications of Boolean functions in system reliability analysis have stimulated considerable efforts to adapt BDD and its extended formats to reliability analysis of complex systems since 1993. These efforts have been firstly expended in reliability analysis of binary-state single-phase systems in which both the system and components exhibit only two states: operational or failed and their behaviors do not change throughout the mission. Many studies showed that in most cases, the BDD-based method requires less memory and computational time than other reliability analysis methods. Subsequently, various forms of decision diagrams have become the state-of-the-art combinatorial models for efficient reliability analysis of a wide range of complex systems, such as phased-mission systems, multi-state systems, fault-tolerant systems with imperfect fault coverage, s

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