Development And Application Of New System Reliability Analysis Methods For Complex Infrastructure Systems
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Development and Application of New System Reliability Analysis Methods for Complex Infrastructure Systems
The failure event of a structure or lifeline network is often described by a complex logical function of multiple component failure events. Despite significant advances in theories on reliability analysis of individual components and their adoption in practice, the critical knowledge and quantitative methods for reliability assessments of complex system events remain elusive, leading to unknown accuracies in the risk assessment. Such a system reliability analysis is computationally challenging, especially when the definition of the system event is complex, the system has a large number of components, or the component events have significant statistical dependence due to common source effects. To overcome these challenges, this study develops two system reliability analysis methods, termed the Matrix-based System Reliability (MSR) Method and the Sequential Compounding Method (SCM), and applies the methods to risk assessment of complex structural systems and lifeline networks. Unlike existing system reliability analysis methods, the MSR method is applicable to any general system events, and can estimate not only system reliability but also component importance measures and parameter sensitivities of system reliability, which are essential metrics for risk-informed decision-making processes. The MSR method is applied to a bridge transportation network, a highway bridge structural system, and truss structures. The method is further developed to achieve improved efficiency using the first- or second-order reliability method; and to evaluate the sensitivity of the system failure probability with respect to parameters that affect the statistical dependence between the components. These further developments are demonstrated by risk assessment of progressive failures of a generalized Daniels system structure and by finite element system reliability analysis of a bridge pylon system. This study also aims at developing new methods for stochastic damage detection of pipeline networks based on the MSR method. The methods allow for efficient uncertainty quantification of system quantities such as network flow measures and for updating the component damage probabilities based on post-disaster observations on network performance. The accuracy and efficiency of these methods are demonstrated by a water pipeline network with 15 pipes that is subjected to an earthquake event. The sequential compounding method (SCM) is also developed to compute the probability of a general system event described in terms of a multivariate normal distribution. The merit of the SCM is its superior efficiency compared to existing system reliability methods including the MSR method. The accuracy and efficiency of the SCM is tested by a wide range of numerical examples including large systems consisting of 1,000 components. Due to its wide applicability, accuracy and efficiency, the method is expected to enhance the computational capability in various applications of system reliability analysis.
Reliability Analysis and Asset Management of Engineering Systems
Author: Escola Politécnica da USP
language: en
Publisher: Elsevier
Release Date: 2021-09-24
Reliability Analysis and Asset Management of Engineering Systems explains methods that can be used to evaluate reliability and availability of complex systems, including simulation-based methods. The increasing digitization of mechanical processes driven by Industry 4.0 increases the interaction between machines and monitoring and control systems, leading to increases in system complexity. For those systems the reliability and availability analyses are increasingly challenging, as the interaction between machines has become more complex, and the analysis of the flexibility of the production systems to respond to machinery failure may require advanced simulation techniques. This book fills a gap on how to deal with such complex systems by linking the concepts of systems reliability and asset management, and then making these solutions more accessible to industry by explaining the availability analysis of complex systems based on simulation methods that emphasise Petri nets. - Explains how to use a monitoring database to perform important tasks including an update of complex systems reliability - Shows how to diagnose probable machinery-based causes of system performance degradation by using a monitoring database and reliability estimates in an integrated way - Describes practical techniques for the application of AI and machine learning methods to fault detection and diagnosis problems
Recent Advances in Multi-state Systems Reliability
This book addresses a modern topic in reliability: multi-state and continuous-state system reliability, which has been intensively developed in recent years. It offers an up-to-date overview of the latest developments in reliability theory for multi-state systems, engineering applications to a variety of technical problems, and case studies that will be of interest to reliability engineers and industrial managers. It also covers corresponding theoretical issues, as well as case studies illustrating the applications of the corresponding theoretical advances. The book is divided into two parts: Modern Mathematical Methods for Multi-state System Reliability Analysis (Part 1), and Applications and Case Studies (Part 2), which examines real-world multi-state systems. It will greatly benefit scientists and researchers working in reliability, as well as practitioners and managers with an interest in reliability and performability analysis. It can also be used as a textbook or as a supporting text for postgraduate courses in Industrial Engineering, Electrical Engineering, Mechanical Engineering, Applied Mathematics, and Operations Research.