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Methods for reliability improvement and risk reduction
\"Comprehensive reference covering methods and principles for reducing risk and improving reliability -Offers an overview of standard methods for reliability improvement and risk reduction -Introduces new methods including separation, segmentation and inverting -Covers a universal set of risk reduction methods and principles which transcend engineering -Includes case studies and application examples Market description: Primary: Engineers in industry, including safety professionals, risk managers, reliability consultants, design engineers and reliability and risk researchers. Secondary: Graduate students in reliability engineering, mechanical engineering, aerospace engineering electrical engineering, electronics, chemical engineering, civil engineering and nuclear engineering\"-- Provided by publisher.
Book
Fault Tolerant Systems
There are many applications in which the reliability of the overall system must be far higher than the reliability of its individual components. In such cases, designers devise mechanisms and architectures that allow the system to either completely mask the effects of a component failure or recover from it so quickly that the application is not seriously affected. This is the work of fault-tolerant designers and their work is increasingly important and complex not only because of the increasing number of “mission critical” applications, but also because the diminishing reliability of hardware means that even systems for non-critical applications will need to be designed with fault-tolerance in mind. Reflecting the real-world challenges faced by designers of these systems, this book addresses fault tolerance design with a systems approach to both hardware and software. No other text on the market takes this approach, nor offers the comprehensive and up-to-date treatment the authors provide. Students, designers and architects of high performance processors will value this comprehensive overview of the field.
eBook
AK-SYSi: an improved adaptive Kriging model for system reliability analysis with multiple failure modes by a refined U learning function
Due to multiple implicit limit state functions needed to be surrogated, adaptive Kriging model for system reliability analysis with multiple failure modes meets a big challenge in accuracy and efficiency. In order to improve the accuracy of adaptive Kriging meta-model in system reliability analysis, this paper mainly proposes an improved AK-SYS by using a refined
U
learning function. The improved AK-SYS updates the Kriging meta-model from the most easily identifiable failure mode among the multiple failure modes, and this strategy can avoid identifying the minimum mode or the maximum mode by the initial and the in-process Kriging meta-models and eliminate the corresponding inaccuracy propagating to the final result. By analyzing three case studies, the effectiveness and the accuracy of the proposed refined
U
learning function are verified.
Journal Article
An active weight learning method for efficient reliability assessment with small failure probability
In current years, the metamodel-based reliability analysis method has been developed to assess the failure probability for engineering problems involving time-consuming computational model. Despite the fact that some sequential metamodel-based reliability analysis methods have improved the computational efficiency, there still exists a certain possibility to further reduce the computational effort without loss of accuracy. In this study, an active weight learning method based upon the Kriging model is well proposed for reliability analysis. An active weight learning function based on the optimization theory is built to replace the traditional learning function, in which the important degrees of sampling points on the limit state function are assigned as different weight indices. The Kriging surrogate model is updated according to the proposed active weight learning function. In addition, the proposed strategy is extended to solve the system reliability problem, which can effectively avoid the nonlinearity of composite function in the traditional approach. A novel stopping criterion is also exploited to guarantee the convergence of the proposed method. Five numerical examples are provided to verify the effectiveness of the proposed method and convergence strategy. Results indicate that the proposed method can significantly improve the computational efficiency of reliability analysis without sacrificing computational accuracy.
Journal Article
Structural and system reliability
\"Based on material taught at the University of California, Berkeley, this textbook offers a modern, rigorous and comprehensive treatment of the methods of structural and system reliability analysis. It covers the first- and second-order reliability methods for components and systems, simulation methods, time- and space-variant reliability, and Bayesian parameter estimation and reliability updating. It also presents more advanced, state-of-the-art topics such as finite element reliability methods, stochastic structural dynamics, reliability-based optimal design, and Bayesian networks. A wealth of well-designed examples connect theory with practice, with simple examples demonstrating mathematical concepts and larger examples demonstrating their applications. End-of-chapter homework problems are included throughout\"-- Provided by publisher.
Book
A system reliability analysis method combining active learning Kriging model with adaptive size of candidate points
This paper investigates the improvement of system reliability analysis (SRA) methods which combine active learning Kriging (ALK) model with Monte Carlo simulation. In this kind of methods, a number of Monte Carlo samples are treated as the candidate points of the ALK models, and the size (or the number) of candidate points vitally affects the efficiency. However, the existing strategies fail to build the Kriging model with the optimal size of candidate points. Therefore, a certain quantity of training points was wasted. To circumvent this drawback, a strategy with an adaptive size of candidate points (ASCP) is exploited and seamlessly integrated into one of the recently proposed ALK model-based SRA method. In this strategy, the optimal size is iteratively predicted and updated according to the predicted information of component Kriging models. After several iterations, the optimal size can be approximately obtained, and the learning process can be executed with an optimal size of candidate points hereafter. Three numerical examples are investigated to demonstrate the efficiency and accuracy of the proposed method.
Journal Article
A new framework of complex system reliability with imperfect maintenance policy
The interactions and dependencies between software and hardware are often neglected in modeling system reliability in the past few decades due to the mathematical complexity. However, many system failures occurred from the interactions or simultaneous occurrences of software and hardware. This paper first proposes a new diagram of categorizing system-level failures and further incorporates such a diagram into the development of complex system reliability framework. System-level failures result from software subsystem, hardware subsystem, and the interactions of software and hardware subsystems. The focus of this study is on the investigation of the interactions failures generated from the interactions of software and hardware subsystems. In addition to the considerations of total hardware failures, software-induced hardware failures, and hardware-induced software failures introduced by Zhu and Pham (Mathematics 7(11):1049, 2019), we further introduce the partial hardware failures that can be respectively induced by hardware and software to explicitly demonstrate the dependencies and interactions between software and hardware. Hence, a new complex system reliability framework is developed based on such system-level failure categorization with the Markov process. Furthermore, the numerical examples are studied to illustrate the impacts on system reliability with the changes of state transition parameters that modeling the interactions of software and hardware subsystems. Finally, we have studied two maintenance policies of the proposed complex system reliability model.
Journal Article
A novel null-hypothesis approximation method of limit state function in multi-failure mode reliability analysis of structural system
Modern industrial products feature complex structures, multiple sub-structural systems, and high-reliability requirements, especially for aerospace products. For the reliability analysis (RA) of these products, even if the analysis error of single sub-structural systems is small, the one of global reliability may be very large, therefore, it is challenging to meet the reliability requirements for whole products. With regard to the high-reliability and multiple failure mode problems, this paper proposes a novel null-hypothesis approximation method (NHA) of limit surface function (LSF). It can obtain the points on LSF in the feasible domain. Then, a new RA framework is established by combining the convex point cloud model (CPCM) with NHA. CPCM can obtain the volume of the point cloud by focusing on the points on LSF in the feasible domain. Compared with traditional RA methods, the new RA framework is suitable for complex structural systems with high-reliability, and it can mitigate the analysis error that is caused by traditional RA methods that only concentrate on curvature in proximity to MPP. Finally, three numerical examples and an engineering example are used to test the performance of the proposed method. Results indicate that the proposed method has similar accuracy to the Monte Carlo simulation (MCS) for rare event probability, and the computational cost is acceptable for complex engineering problems.
Journal Article