System reliability when components can be swapped upon failure

Resilience of systems to failures during functioning is of great practical importance. One of the strategies that might be considered to enhance reliability and resilience of a system is swapping components when a component fails, thus replacing it by another component from the system that is still functioning. This thesis studies this scenario, particularly with the use of the survival signature concept to quantify system reliability, where it is assumed that such a swap of components requires these components to be of the same type. We examine the effect of swapping components on a reliability importance measure for the specific components, and we also consider the joint reliability importance of two components. Such swapping of components may be an attractive means toward more resilient systems and could be an alternative to adding more components to achieve redundancy of repair and replacement activities. Swapping components, if possible, is likely to incur some costs, for example for the actual swap or to prepare components to be able to take over functionality of another component. In this thesis we also consider the cost effectiveness of component swapping over a fixed period of time. It is assumed that a system needs to function for a given period of time, where failure to achieve this incurs a penalty cost. The expected costs when the different swap scenarios are applicable are compared with the option not to enable swaps. We also study the cost effectiveness of component swapping over an unlimited time horizon from the perspective of renewal theory. We assume that the system is entirely renewed upon failure, at a known cost, and we compare different swapping scenarios. The effect of components swapping on preventive replacement actions is also considered. In addition, we extend the approach of component swapping and the cost effectiveness analysis of component swapping to phased mission system. We consider two scenarios of swapping possibilities, namely, assuming that the possibilities of component swapping can occur at any time during the mission or only at transition of phases

The survival signature for quantifying system reliability: an introductory overview from practical perspective

The structure function describes the functioning of a system dependent on the states of its components, and is central to theory of system reliability. The survival signature is a summary of the structure function which is sufficient to derive the system’s reliability function. Since its introduction in 2012, the survival signature has received much attention in the literature, with developments on theory, computation and generalizations. This paper presents an introductory overview of the survival signature, including some recent developments. We discuss challenges for practical use of survival signatures for large systems

ENSURING SURVIVABILITY FOR NAVAL SPECIAL WARFARE OPERATIONS IN THE ARCTIC

Naval Special Warfare (NSW) operators are not currently manned, trained, or equipped to effectively survive or execute High Arctic mission sets. The dynamic rate of environmental change and the adversarial exploitation of the Arctic regions have disadvantaged the United States and its allies. This capstone intends to reduce inherent survival risks an NSW operator would incur associated with extreme “cold” and increase the duration an NSW operator can remain on station in the High Arctic. The end state is to provide NSW with research and a Course of Action (COA) that leads to prototype production, orchestrated through the Defense Innovation Unit (DIU), enabling NSW operators to rapidly respond to crisis/conflict in all Arctic regions.Lieutenant, United States NavyApproved for public release. Distribution is unlimited

Uncertainty in Engineering

This open access book provides an introduction to uncertainty quantification in engineering. Starting with preliminaries on Bayesian statistics and Monte Carlo methods, followed by material on imprecise probabilities, it then focuses on reliability theory and simulation methods for complex systems. The final two chapters discuss various aspects of aerospace engineering, considering stochastic model updating from an imprecise Bayesian perspective, and uncertainty quantification for aerospace flight modelling. Written by experts in the subject, and based on lectures given at the Second Training School of the European Research and Training Network UTOPIAE (Uncertainty Treatment and Optimization in Aerospace Engineering), which took place at Durham University (United Kingdom) from 2 to 6 July 2018, the book offers an essential resource for students as well as scientists and practitioners

Network reliability analysis through survival signature and machine learning techniques

As complex networks become ubiquitous in modern society, ensuring their reliability is crucial due to the potential consequences of network failures. However, the analysis and assessment of network reliability become computationally challenging as networks grow in size and complexity. This research proposes a novel graph-based neural network framework for accurately and efficiently estimating the survival signature and network reliability. The method incorporates a novel strategy to aggregate feature information from neighboring nodes, effectively capturing the response flow characteristics of networks. Additionally, the framework utilizes the higher-order graph neural networks to further aggregate feature information from neighboring nodes and the node itself, enhancing the understanding of network topology structure. An adaptive framework along with several efficient algorithms is further proposed to improve prediction accuracy. Compared to traditional machine learning-based approaches, the proposed graph-based neural network framework integrates response flow characteristics and network topology structure information, resulting in highly accurate network reliability estimates. Moreover, once the graph-based neural network is properly constructed based on the original network, it can be directly used to estimate network reliability of different network variants, i.e., sub-networks, which is not feasible with traditional non-machine learning methods. Several applications demonstrate the effectiveness of the proposed method in addressing network reliability analysis problems

Technology for the Future: In-Space Technology Experiments Program, part 2

The purpose of the Office of Aeronautics and Space Technology (OAST) In-Space Technology Experiments Program In-STEP 1988 Workshop was to identify and prioritize technologies that are critical for future national space programs and require validation in the space environment, and review current NASA (In-Reach) and industry/ university (Out-Reach) experiments. A prioritized list of the critical technology needs was developed for the following eight disciplines: structures; environmental effects; power systems and thermal management; fluid management and propulsion systems; automation and robotics; sensors and information systems; in-space systems; and humans in space. This is part two of two parts and contains the critical technology presentations for the eight theme elements and a summary listing of critical space technology needs for each theme

IMPORTANCE MEASURE OF PROBABILISTIC COMMON CAUSE FAILURES UNDER SYSTEM HYBRID UNCERTAINTY BASED ON BAYESIAN NETWORK

When dealing with modern complex systems, the relationship existing between components can lead to the appearance of various dependencies between component failures, where multiple items of the system fail simultaneously in unpredictable fashions. These probabilistic common cause failures affect greatly the performance of these critical systems. In this paper a novel methodology is developed to quantify the importance of common cause failures when hybrid uncertainties are presented in systems. First, the probabilistic common cause failures are modeled with Bayesian networks and are incorporated into the system exploiting the α factor model. Then, probability-boxes (bound analysis method) are introduced to model the hybrid uncertainties and quantify the effect of uncertainties on system reliability. Furthermore, an extended Birnbaum importance measure is defined to identify the critical common cause failure events and coupling impact factors when uncertainties are expressed by probability-boxes. Finally, the effectiveness of the method is demonstrated through a numerical example.W przypadku nowoczesnych systemów złożonych, relacje zachodzące między komponentami mogą prowadzić do pojawienia się różnych zależności między ich uszkodzeniami, a tym samym do sytuacji w których kilka składowych systemu ulega uszkodzeniu jednocześnie w nieprzewidywalny sposób. Tego typu probabilistyczne uszkodzenia wywołane wspólną przyczyną (PCCF) mają ogromny wpływ na wydajność tych kluczowych systemów. W przedstawionym artykule opracowano nową metodę szacowania ważności PCFF w sytuacjach, gdy w systemie występują niepewności hybrydowe. W pierwszej kolejności, PCFF zamodelowano za pomocą sieci bayesowskich i włączono do systemu wykorzystującego model współczynnika α. Następnie, wprowadzono przedziały prawdopodobieństwa, tzw. probability boxes (bound analysis method), w celu zamodelowania niepewności hybrydowych i kwantyfikacji wpływu tych niepewności na niezawodność systemu. Ponadto zdefiniowano rozszerzoną miarę ważności Birnbauma, która pozwala zidentyfikować krytyczne zdarzenia PCCF oraz czynniki, które je wywołały, w przypadkach, gdy niepewności wyrażone są za pomocą probability boxes. Skuteczność metody wykazano na przykładzie numerycznym

Phased-Mission Reliability and Importance Measure Analysis for Linear and Circular UAV Swarms

The phased-mission reliability of unmanned aerial vehicle (UAV) swarm refers to its capability to successfully complete the missions of each phase under specified conditions for a specified period. In order to study the reliability of phased-mission in UAV swarm, this paper firstly studies the reliability of a single UAV under fault coverage. Then, considering the mission characteristics of UAV swarm, the consecutive k-out-of-n system is studied to model and predict the reliability of UAV swarm phase mission. Some importance measures are introduced to analyze the influence of UAV in different positions on the reliability of the whole system. Finally, numerical examples of linear and circular UAV swarms are given to demonstrate and verify the correctness of the model. The reliability modeling established in this paper can predict the phased-mission reliability of UAV swarm scientifically

Uncertainty in Engineering

This open access book provides an introduction to uncertainty quantification in engineering. Starting with preliminaries on Bayesian statistics and Monte Carlo methods, followed by material on imprecise probabilities, it then focuses on reliability theory and simulation methods for complex systems. The final two chapters discuss various aspects of aerospace engineering, considering stochastic model updating from an imprecise Bayesian perspective, and uncertainty quantification for aerospace flight modelling. Written by experts in the subject, and based on lectures given at the Second Training School of the European Research and Training Network UTOPIAE (Uncertainty Treatment and Optimization in Aerospace Engineering), which took place at Durham University (United Kingdom) from 2 to 6 July 2018, the book offers an essential resource for students as well as scientists and practitioners

TRIAD - Preliminary design of an operational earth resources survey system Final report

Design of operational earth resources survey syste