Modern aircrafts are evolving toward more electric aircraft (MEA), resulting in greater reliance on the electrical system for safe flight. On-board power system of MEA integrates a large number of ...power electronic converters, and it is reported that semiconductor devices and electrolytic capacitors in power converters are the most vulnerable links impacted by loading conditions; thus, reliability becomes a critical concern in an MEA power system. This paper proposes a hierarchical approach for systematic reliability modeling and evaluation for the on-board power system of MEAs. It consists of three hierarchical levels (HLs): component level (HL1), subsystem level (HL2), and system level (HL3). In HL1, failure rates of power electronic components are modeled considering relevant inner structure and loading conditions; in HL2, the reliability of individual subsystems such as converters are constructed; in HL3, the system reliability is quantified based on the network architecture and reliability of the subsystems. The impacts of different parts (components/subsystems) on the overall system are assessed effectively with the identification of the vulnerable parts. This also provides a guideline for reliability enhancement by using thermal control techniques, adding redundancies or performing maintenance on the vulnerable parts to ensure the satisfactory of system reliability requirements. The proposed method is demonstrated on the future MEA power system architectures (hybrid ac-dc architecture and HVdc architecture).
•Complex system reliability analysis using path testing and decomposition techniques.•Predict/analyze the complete system reliability without testing the entire system.•Accurate software architecture ...quality prediction during design phase.•Less complexity achieved using sequential path execution.•Predicting entire software system reliability with highest test coverage.
With the increasing needs of the people in this generation, a large number of highly featured quality software systems need to be developed in all the domains. Nowadays complexity of the software system is gradually increased because of its large size. With this nature, the traditional software development process unable to produce higher quality software system within limited resources. So that, the traditional software development process has been moved to the reuse based component based software development (CBSD) which reduces the time and resource of software development. Testing is the important process in the software development life cycle to ensure the reliability or quality of software systems. Lots of reliability models have been developed to predict the software system reliability in the earlier stages of development. But these existing reliability analysis models are insufficient to estimate the reliability of component based software system (CBSS) within the limited resources. To solve this issue, the new approach was introduced by many researchers based on software architecture to estimate the reliability of component based software system. Based on that, we have proposed new framework centered on path testing to predict the reliability of the CBSS. Here we have chosen three test paths (simple, medium and complex structure) from the system for reliability estimation instead of taking all the paths. Then independent simple paths have been identified from the chosen medium and complex path to reduce the complexity of reliability estimation. All the simple paths are executed sequentially to estimate its reliability. Actual software system reliability will be predicted based on the estimated path reliability. The ATM case study has been taken to validate the proposed framework. The result obtained from this experiment is compared with the standard baseline models CUORM, LCBRM and Chao-Jung to prove the accuracy and efficiency of our proposed model. The result shows that, our proposed framework has the acceptable accuracy compared to the other models.
The electrical power grid is a critical infrastructure that plays a key role in supporting modern society. The reliability of power systems needs to be continuously maintained to deliver high-quality ...electric services. Due to the tremendous amounts of potential investment demanded for constructing new electricity transmission facilities, electric utilities need economical solutions that can enable them to supply electricity to their customers in a cost-effective and reliable way. Dynamic thermal rating (DTR) and network topology optimization (NTO) technologies aim to maximize the use of existing transmission assets and to provide flexible ways to enhance reliability of the power system. In this study, the DTR and NTO are incorporated into the power grid reliability assessment procedure using the sequential Monte Carlo simulation. Multiple case studies are carried out based on the modified IEEE RTS-79 and IEEE RTS-96 systems, accounting for long-term multiarea weather conditions. The numerical results indicate that with the incorporation of DTR and NTO, the reliability of power systems can be improved. The effect of these methods is especially significant for power grids with lower electricity delivery capabilities.
•First order reliability method (FORM) is improved using conjugate search direction.•Two conjugate methods are proposed using self-adaptive (SAC) and hybrid self-adaptive (HSAC) conjugate ...formula.•The SAC and HSAC are adaptively computed using the FR conjugate method in order to satisfy the sufficient descent condition.•Efficiency and robustness of SAC and HSAC are tested using seven reliability problems.•Proposed SAC and HSAC methods are shown the remarkable efficiency and robustness compared to the existing FORM formula.
The traditional First Order Reliability Method (FORM) using steepest descent search direction may yield unstable solutions due to periodic nature and chaos for reliability analysis problems involving highly nonlinear performance functions. A conjugate search direction approach is attempted in the present study to overcome such problem of the FORM for Most Probable Point (MPP) search. Two iterative FORM schemes are investigated based on conjugate descent direction using self-adaptive conjugate (SAC) and hybrid self- adaptive conjugate (HSAC) search directions for estimating reliability index. The SAC is proposed using Fletcher and Reeves (FR) method and an adaptive conjugate scalar factor to improve the efficiency of the FR method for reliability analysis of highly nonlinear performance function. The HSAC is adaptively computed using FR and SAC methods to improve the robustness and efficiency of the FORM formula. The effectiveness of the proposed SAC and HSAC approaches are studied compare to the traditional FORM algorithms through several numerical examples. The proposed methods based on conjugate search direction are found to be more efficient and robust than the usual FORM algorithms.
•A new binary-addition tree algorithm (BAT) called the AppBAT is proposed.•A new lower-bound of the network reliability is proposed using the AppBAT.•AppBAT outperforms Monte Carlo Simulations (MCS) ...and other reliability bound algorithms.•AppBAT can overcome the obstacle of NP-hardness by finding a good solution.•AppBAT also can be improved by combining MCS-based algorithms.
Real-world applications such as the internet of things, wireless sensor networks, smart grids, transportation networks, communication networks, social networks, and computer grid systems are typically modeled as network structures. Network reliability represents the success probability of a network and it is an effective and popular metric for evaluating the performance of all types of networks. Binary-state networks composed of binary-state (e.g., working or failed) components (arcs and/or nodes) are some of the most popular network structures. The scale of networks has grown dramatically in recent years. For example, social networks have more than a billion users. Additionally, the reliability of components has increased as a result of both mature and emergent technology. For highly reliable networks, it is more practical to calculate approximated reliability, rather than exact reliability, which is an NP-hard problem. Therefore, we propose a novel direct reliability lower bound based on the binary addition tree algorithm called AppBAT to calculate approximate reliability. The efficiency and effectiveness of the proposed reliability bound are analyzed based on time complexity and validated through numerical experiments.
Analytical methods for evaluating the reliability of simple and radial distribution networks have been well established. Since these analytical methods cannot consider post-fault load transfer ...between feeders, the reliability indices are significantly underestimated for mesh-constructed distribution networks. To accommodate various application scenarios, Monte-Carlo simulations are widely used for complex distribution networks and heavy computation burden is involved. In this paper, we propose a novel linear programming model which includes precisely assessing reliability and considers post-fault network reconfiguration strategies involving operational constraints. Moreover, this model also can formulate the influences of demand variations, uncertainty of distributed generations and protection failures on the reliability indices. Numerical simulations show that the proposed model yields the same results as the simulation-based algorithm. Specifically, the system average interruption duration indices are reduced when considering post-fault network reconfiguration strategies in all tested systems. Moreover, the proposed model is suitable for inclusion in reliability-constrained operational and planning optimization models for power distribution systems.
In reliability engineering, each component of the system, in addition to internal failures, is usually subject to lethal external shocks. Usually, the random shocks are produced by a random ...environment, and modeled by a stochastic process. In this paper, using the theory of signature and survival signature depending on whether the components of the system are of one type or several types, a general approach in assessing the system reliability is presented. We also propose some preventive maintenance strategies for a multi-component system whose components are subject to both internal failures and fatal shocks. In each policy, based on the maintenance costs of the system and its components, a cost function is computed as a criterion to be optimized. The signature-based representation of the system reliability is especially useful to maintain the system in optimal conditions by considering the component repair costs. To illustrate the theoretical results, extensive graphical and numerical examples are presented.
•A general approach in assessing the system reliability is presented.•The system is subject to internal failures and external shocks.•The theory of signature and survival signature is used.•Some optimal maintenance models are proposed.
•Joint importance measures for the optimal structure in multistate systems are extended.•Behaviors of joint importance measures are reflected by the optimal component sequence.•Properties of joint ...importance with the changes of component reliabilities are analyzed.•Change in joint importance with respect to the optimal system structure is presented.•Differences of generalized measures between the large system and small system are illustrated.
For a multi-component system, the impact of the alteration of components on the system reliability often needs assessment. Existing importance measures, however, do not consider the impact of the possible change of the system structure during its life cycle. Therefore, relevant factors should be considered to better reflect the changes in system reliability. To this end, this paper proposes joint importance measures for the optimal component sequence of a consecutive-k-out-of-n system. Incorporating the generalized measures, the paper obtains the joint integrated importance measure and the joint differential importance measure for the optimal component sequence in the binary and multistate consecutive-k-out-of-n systems. Then some properties of the proposed joint importance measures for optimal component sequence are analyzed. Furthermore, this measure reveals the relationship between component reliability and joint importance measure under consideration of consecutive-k-out-of-n system structure changes. Finally, numerical examples are given to demonstrate the applicability of the proposed measures.
Autonomous vehicles are expected to emerge as a main trends in vehicle development over the next decade. To support autonomous vehicles, ultra-reliable low-latency communications (URLLC) is required ...between autonomous vehicles and infrastructure networks, e.g., a fifth-generation (5G) cellular networks. Hence, reliability and latency must be jointly investigated in 5G autonomous vehicular networks. In this paper, utilizing the Euclidean norm theory, we first propose a reliability and latency joint function to evaluate the joint impact of reliability and latency in 5G autonomous vehicular networks. The interactions between reliability and latency are illustrated via Monte Carlo simulations of 5G autonomous vehicular networks. To improve both the reliability and latency performance and implement URLLC, a new network slicing solution that extends from resource slicing to service and function slicing is presented for 5G autonomous vehicular networks. The simulation results indicate that the proposed network slicing solution can improve both the reliability and latency performance and ensure URLLC in 5G autonomous vehicular networks.
•A reliability method combining adaptive global metamodel and probability density evolution method is proposed.•Adaptive sampling technique for global metamodeling is more superior to one-stage ...sampling technique.•The AGM-PDEM has a better applicability than the AGM-MCS.•Accuracy of reliability method can be guaranteed by specifying moderate stopping condition in adaptive sampling process.
An efficient reliability method, i.e., AGM-PDEM, is presented which combines the adaptive global metamodel (AGM) and the probability density evolution method (PDEM). The global metamodel is firstly constructed using an adaptive sampling technique and employed to represent the relationship between the equivalent extreme value (EEV) of responses and basic random variables of stochastic systems. The EEVs of representative point sets with enlarged size are approximated by the constructed surrogate model. Reliability of the stochastic systems then can be readily obtained by virtue of the PDEM. To demonstrate the accuracy and efficiency of the proposed method in metamodeling response surface and in assessing system reliability, the global metamodeling of three analytical functions with different nonlinear features, and the reliability analysis of an eight-story shear frame with different random parameters related to the performance level are addressed. A comparative study against the direct Monte Carlo simulation (MCS) with the established metamodel is carried out as well. Numerical results demonstrate that the adaptive sampling technique for global metamodeling is more superior by comparison with the one-stage sampling technique. The AGM-PDEM has a better applicability than the AGM-MCS in dealing with the problems of both moderate and small failure probabilities encountered in engineering structures. Furthermore, both the fidelity of global metamodel and the accuracy of the proposed reliability method can be guaranteed by specifying a moderate stopping condition in the adaptive sampling process. Therefore, the proposed AGM-PDEM exhibits a satisfactory accuracy and a high efficiency for the reliability assessment of structural systems in practice.
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