Maximize System Reliability Research Articles

A Review on Optimal Allocation and Sizing Techniques for DG in Distribution Systems

Distributed Generation (DG) offers the reliable and economic source of electricity to consumers. These are connected directly to the distribution system at consumer load points. Integration of DG units into an existing system has significantly high importance due to its innumerable advantages. However, Optimal DG (ODG) allocation and sizing is always a challenging task for utilities as well as consumers. The major objective of ODG allocation and sizing is to improve system overall efficiency with minimum power loss, maximum system security, voltage stability, and reliability. Analytical techniques are performing well for small and simple systems, not suitable for a system with large and complex networks. However, various meta-heuristic techniques are performing better in terms of accuracy and convergence for extensively large and complex networks. A hybrid optimization is a combination of two or more optimization techniques. This technique offers efficient and reliable global optimum solutions for complex multi-objective problems. In this context, a comprehensive literature review of DG fundamentals and the different technical approaches for DG integration into the distribution system are analyzed here. Furthermore, an attempt has been made for comparison of analytical, classical (non-heuristic), meta-heuristic and hybrid optimization techniques with respect to objective function, test system, advantages, and disadvantages. This present study will give in-depth knowledge and acts as a forthright reference for imminent investigators and investors for ODG allocation and sizing in a distribution system.Â

Group maintenance strategy for stochastic selective maintenance

Selective maintenance is often applied to many industrial environments when the maintenance actions are performed between sequence missions. When the length of maintenance or work mission time is stochastic and there are multiple maintenance workers with different capacity, previous works to determine maintenance and mission times are unsuitable for it. Assuming that the time of work mission is random and maintenance time is determined, to maximise system reliability of next work mission, this paper proposes a stochastic model for a two-state system comprising several series-parallel components with multiple maintenance workers under the maintenance time limit. The optimal maintenance plan is obtained by hybrid intelligent optimisation algorithm. The validity and feasibility of the model are verified by the simulation.

Multi-objective optimization of reliability–redundancy allocation problem with cold-standby strategy using NSGA-II

The reliability–redundancy allocation problem (RRAP) involves the selection of components reliability and redundancy levels, with the aim of maximizing system reliability. Generally, RRAPs are formulated with the assumption of active redundancy strategy. In this paper, the problem is formulated by considering a cold-standby strategy. In most practical cases, complicated decision-making problems are encountered due to the presence of several conflicting objectives such as maximizing system reliability and simultaneously minimizing the total cost, weight or volume of the systems. Therefore, it is essential to consider these conflicting criteria as the main objectives of the optimization problem. Hence, in this paper, a bi-objective RRAP is developed. The proposed mathematical model is developed for three benchmark problems and solved by using a multi-objective evolutionary algorithm (NSGA-II). In order to calculate the exact reliability values for the problems, a recently introduced Markov-based approach is used. The results indicate that, in comparison with recent studies, the proposed NSGA-II shows superior performance and leads to better results in all benchmark problems. In other words, for each problem, a variety of structures are presented which outperform the best active structure and at least one structure is obtained that outperforms the best standby one in the literature.

An integrated approach to redundancy allocation and test planning for reliability growth

Previously, two strategies were disjointly considered for improving system reliability: testing components (to identify and remove failure modes, resulting in reliability growth) and installing redundancies. In this paper, we develop a new model that merges these concepts within an integrated optimization model that maximizes system reliability. Specifically, our model considers a series-parallel system in which the system reliability can be improved by both testing components and installing redundant components. We contribute an exact algorithm that decomposes the problem into smaller integer linear programs. We prove that this algorithm is finite and apply it to a set of instances. Experiments demonstrate that the integrated approach generates greater reliabilities than applying test planning and redundancy allocation models iteratively, and moreover, the integrated approach yields significant savings in computational time.

Robust allocation of testing resources in reliability growth

Reliability growth testing seeks to improve system reliability by identifying and removing failure modes. Recent models maximize system reliability by allocating limited testing resources across the system’s components, each of which exhibits reliability growth according to the AMSAA model (Crow, 1974) with known parameters. We extend this research to solve a robust version of this problem for both series and series-parallel systems in which AMSAA parameters are uncertain but assumed to lie within a budget-restricted uncertainty set. We develop and analyze an exact solution approach for this problem based on a cutting-plane algorithm. In the case of series systems, we demonstrate that the subproblems from this algorithm are efficiently solvable via dynamic programming. Computational results demonstrate (i) the value of the robust optimization approach as compared to deterministic alternatives and (ii) the efficacy of our algorithm.

A State Transfer Scheduling Optimization Framework for Standby Systems

Standby techniques of different types have been applied in a wide range of industries to improve system reliability. Since the reliabilities of the operating components are generally high in the initial period of the mission and the standby components are hardly needed, it is more reasonable to set a standby component into cold standby state at the beginning and switch it into the warm standby state after certain period. In this paper, we consider 1-out-of-N: G standby systems with components whose lifetimes can follow general distributions and investigate the optimal state transfer scheduling problem with the objective of maximizing system reliability at mission time. For system reliability evaluation, the system reliability functions are derived in a recursive way and the numerical methods based on the closed Newton-Cotes quadrature rules are proposed without using derivatives. The optimal state transfer scheduling is derived by using the meta-heuristic differential evolution algorithm. By identifying the optimal state transfer scheduling, the state transfer order of standby components can also be determined. Two numerical examples are provided to illustrate the proposed methodology and demonstrate its effectiveness.

Maximization of system reliability with the consideration of component sequencing

This article deals with the optimal reliability design for a system with heterogeneous components, i.e., redundancy allocation problem with mixed components (RAPMC). Furthermore, it is confirmed that the component sequence affects the reliability of a cold-standby system with an imperfect fault detector/switch, and the optimal order depends on the mission time of the system, the reliabilities of a switch and components. Thus, the suggested RAPMC includes a new decision variable for the optimal component order. The usefulness of the RAPMC has been demonstrated by comparing the optimal system reliabilities by it and other RAPs in a well-known example. That is, under the same constraints on system specifications such as cost and weight, it recommended the system configuration with higher reliability than the previous version of RAPs. Finally, introducing the notion of the optimal component sequence for cold-standby subsystems results in a meaningful improvement in the system reliability.

Holistic energy and failure aware workload scheduling in Cloud datacenters

The global uptake of Cloud computing has attracted increased interest within both academia and industry resulting in the formation of large-scale and complex distributed systems. This has led to increased failure occurrence within computing systems that induce substantial negative impact upon system performance and task reliability perceived by users. Such systems also consume vast quantities of power, resulting in significant operational costs perceived by providers. Virtualization – a commonly deployed technology within Cloud datacenters – can enable flexible scheduling of virtual machines to maximize system reliability and energy-efficiency. However, existing work address these two objectives separately, providing limited understanding towards studying the explicit trade-offs towards dependable and energy-efficient compute infrastructure. In this paper, we propose two failure-aware energy-efficient scheduling algorithms that exploit the holistic operational characteristics of the Cloud datacenter comprising the cooling unit, computing infrastructure and server failures. By comprehensively modeling the power and failure profiles of a Cloud datacenter, we propose workload scheduling algorithms Ella-W and Ella-B, capable of reducing cooling and compute energy while minimizing the impact of system failures. A novel and overall metric is proposed that combines energy efficiency and reliability to specify the performance of various algorithms. We evaluate our algorithms against Random, MaxUtil, TASA, MTTE and OBFIT under various system conditions of failure prediction accuracy and workload intensity. Evaluation results demonstrate that Ella-W can reduce energy usage by 29.5% and improve task completion rate by 3.6%, while Ella-B reduces energy usage by 32.7% with no degradation to task completion rate.

Reliability optimization for non-repairable series-parallel systems with a choice of redundancy strategies: Erlang time-to-failure distribution

This article deals with a new redundancy allocation model for non-repairable series-parallel systems with multiple strategy choices. The proposed model simultaneously determines the type of components, number of active and standby components to maximize system reliability subject to design constraints. Traditionally, due to complexity and difficulty in obtaining the closed form version of system reliability, a convenient lower-bound on system reliability has been widely applied to approximate it. Assuming that switching mechanism time-to-failure is exponentially distributed, the closed form version of the reliability of subsystems with cold standby redundancy is derived analytically for the first time. This is successfully performed using Markov process and solving the relevant set of differential-difference equations. With respect to the obtained formulation, a semi-analytical expression for the reliability of subsystems with mixed redundancy strategy is also extracted. Component time-to-failure is assumed to follow an Erlang distribution which is suitable for most engineering design problems. The presented model is linear and in the form of standard zero-one integer programs and thus using integer programming algorithms guarantees optimal solutions. The computational results of solving a well-known example indicate the high performance of the proposed model in improving system reliability.

Optimal reliability design of a system with k-out-of-n subsystems considering redundancy strategies

This study presents new reliability models for k-out-of-n systems using a structured continuous-time Markov chain. The approach makes it comfortable to identify the characteristics of the lifetime of the system, such as reliability and expected lifetime. To demonstrate the advantage, the analysis results of them for the multi-spectral camera system, which is the only payload of Korea multi-purpose satellite-2 as an example of a real system. Furthermore, existing studies on a k-out-of-n system with standby redundancy has provided the approximated reliability for it. In this paper, it is confirmed that the approximation error could have an effect on the reliability design for a system. Moreover, new versions of reliability optimization problems, redundancy allocation problem (RAP) and reliability-redundancy allocation problem(RRAP), are proposed. In order to maximize system reliability, they further determine the redundancy strategy, either active or standby redundancy for each k-out-of-n subsystem from the traditional problems. A parallel genetic algorithm is proposed for an RRAP modeled by nonlinear mixed integer programming.

Improving reliability and reducing risk by minimising the rate of damage accumulation

The paper introduces the principle of minimised rate of damage accumulation as a domain-independent principle of reliability improvement and risk reduction. A classification is proposed of methods for reducing the rate of damage accumulation. The paper introduces the method of substitution for reducing the rate of damage accumulation. The original assembly/system is substituted with assembly/system performing the same function and based on different physical principles. Such a substitution often eliminates failure modes characterised by intensive damage accumulation. One of the methods discussed is an optimal replacement resulting in the smallest rate of damage accumulation and maximum system reliability. A method for achieving the smallest rate of damage accumulation for a system with components logically arranged in series has been proposed for the first time. A dynamic programming algorithm for determining the optimal variation of multiple damage-inducing factors to minimise the rate of damage accumulation has also been proposed for the first time. The paper shows that the necessary and sufficient condition for using the additivity rule for calculating the threshold of accumulated damage precipitating failure is the factorisation of the rate of damage accumulation into a function of the amount of damage and a function of the damage-inducing factor.

Birnbaum importance based heuristics for multi-type component assignment problems

This paper proposes a new type of system reliability design problem, namely, the multi-type component assignment problem (MCAP). The MCAP is to assign more than one type of components to positions in a system with the objective of maximizing system reliability. Each type of components has multiple units, and a unit of a particular type can be assigned to a position of the same type in the system. We present the Birnbaum-importance based local search methods and the Birnbaum-importance based genetic algorithms for addressing the MCAP. The comprehensive numerical experiments are conducted for the Lin/Con/k/n:F(G) systems with multiple types of components and four cases of component reliability. The computational results demonstrate the efficiencies of the heuristics, highlighting the methods of dealing with multiple types of components. It shows that the Birnbaum importance in designing the algorithms is promising because of its effectiveness.

A new model for the redundancy allocation problem with component mixing and mixed redundancy strategy

This paper develops a new model for redundancy allocation problem. In this paper, like many recent papers, the choice of the redundancy strategy is considered as a decision variable. But, in our model each subsystem can exploit both active and cold-standby strategies simultaneously. Moreover, the model allows for component mixing such that components of different types may be used in each subsystem. The problem, therefore, boils down to determining the types of components, redundancy levels, and number of active and cold-standby units of each type for each subsystem to maximize system reliability by considering such constraints as available budget, weight, and space. Since RAP belongs to the NP-hard class of optimization problems, a genetic algorithm (GA) is developed for solving the problem. Finally, the performance of the proposed algorithm is evaluated by applying it to a well-known test problem from the literature with relatively satisfactory results.

Reliability Versus Expected Mission Cost and Uncompleted Work in Heterogeneous Warm Standby Multiphase Systems

This paper considers 1-out-of- ${N}$ : G warm standby (WS) systems subject to multiple-phased mission requirements, where performance, failure behavior, operation cost, replacement time, and cost of system elements can vary from phase to phase due to changing working conditions and stress levels. The system succeeds if its elements can accomplish a specified mission task within the maximum allowed time. A numerical algorithm is proposed for evaluating mission indices, including system reliability, expected mission cost, and expected uncompleted work of the heterogeneous, dynamic standby system considered. The influence of the maximum allowed mission time on mission indices is investigated. As demonstrated through examples, the proposed algorithm can also facilitate a determination of mission interruption based on reliability or cost-oriented criteria. Based on the proposed algorithm, an unconstrained optimal standby element sequencing problem is then formulated and solved for heterogeneous WS multiphase systems. The problem is to find the optimal activation sequence of system elements maximizing system reliability, or minimizing expected mission cost, or minimizing expected uncompleted work. The constrained optimization problems of minimizing expected mission cost subject to providing a desired level of mission reliability or uncompleted work are also solved. Illustrative examples of these optimization problems and their applications in estimating the role of each element in the mission success are presented.

A robust reliability redundancy allocation problem under abnormal external failures guided by a new importance measure

Besides inherent failures under normal conditions studied by classical reliability engineering, systems may also be subject to abnormal external failures, whose probabilities are hard, if not impossible, to predict, such as failures caused by natural disasters (e.g. earthquakes, floods, and tornados) and malicious attacks. Thus, abnormal external failures need to be considered when designing a system, as in reliability redundancy allocation problem. To deal with the uncertainty of abnormal external failures and based on robust optimization, this research proposes a multi-objective optimization approach that can simultaneously maximize system reliability under both normal and the worst cases of abnormal external failures for redundancy allocation problem. Since the amount of abnormal external failures is usually unknown, the worst case for each possible amount of abnormal external failures needs to be identified first. For this purpose, a new importance measure that can quantify the importance of a subset of system components with arbitrary cardinality is proposed. We also revise a widely used multi-objective evolutionary algorithm, multi-objective probabilistic solution discovery algorithm, to deal with the complexity of redundancy allocation problem. The merits of the revised algorithm are demonstrated by extensive experiments.

Comprehensive Approach to Allocate Reliability and Cost in Passenger Rail System Design

Rail system design and procurement comprise the process of identifying, acquiring, selecting, and purchasing the right products to form a rail system. To acquire a new system, various products with specific costs and reliability for each subsystem and Corresponding components are chosen from equipment suppliers. Planners must carefully examine the trade-offs between life-cycle cost (LCC), system reliability, and service reliability to allocate resources optimally. This study developed a comprehensive allocation process with four types of optimization models for passenger rail system design: ( a) maximization of system reliability, ( b) maximization of service reliability, ( c) minimization of LCC, and ( d) minimization of a combination of service unreliability (delay cost) and LCC. On the basis of the characteristics of a passenger rail system and possible alternatives, the proposed process can allocate LCC and service reliability optimally to determine the ideal investment plan for rail system design. Empirical case studies have demonstrated that the proposed optimization process and models can evaluate efficiently and successfully all possible alternatives and determine the best allocation among all subsystems and Corresponding components. This comprehensive approach can help users identify the ideal balance between cost and reliability to achieve an optimal rail system design.

A Reliability-aware Task Scheduling Algorithm Based on Replication on Heterogeneous Computing Systems

Over the past several years, a heterogeneous computing (HC) system has become more competitive as a commercial computing platform than a homogeneous system. With the growing scale of HC systems, network failures become inevitable. To achieve high performance, communication reliability should be considered while designing reliability-aware task scheduling algorithms. In this paper, we propose a new algorithm called RMSR (Replication-based scheduling for Maximizing System Reliability), which incorporates task communication into system reliability. To maximize communication reliability, an improved algorithm which searches all optimal reliability communication paths for current tasks is proposed. During the task replication phase, the task reliability threshold is determined by users and each task has dynamic replicas. Our comparative studies for both randomly generated graphs and application graphs of real-world problems show that our RMSR algorithm outperforms existing scheduling algorithms in terms of system reliability. For randomly generated graphs, several factors affecting the performance are analyzed in the paper. For an application graph of a real-world problem with a fixed DAG, the system reliability of the RMSR algorithm is at most influenced by one factor.

An evolutionary computation approach to solving repairable multi-state multi-objective redundancy allocation problems

The redundancy allocation problem (RAP) is an optimization problem for maximizing system reliability at a predetermined time. Among the several extensions of RAPs, those considering multi-state and repairable components are the closest ones to real-life availability engineering problems. However, despite their practical implications, this class of problems has not received much attention in the RAP literature. In this paper, we propose a multi-objective nonlinear mixed-integer mathematical programming to model repairable multi-state multi-objective RAPs (RMMRAPs) where a series of parallel systems experiencing repairs, partial failures, and component degrading through time is considered. The performance of a component depends on its state and may decrease/increase due to minor and major failures/repairs which are modeled by a Markov process. The proposed RMMRAP allows for configuring multiple components and redundancy levels in each sub-system while evaluating multiple objectives (i.e., availability and cost). A customized version of the non-dominated sorting genetic algorithm (NSGA-II), where constraints are handled using a combination of penalty functions and modification strategies, is introduced to solve the proposed RMMRAP. The performance of the proposed NSGA-II and that of an exact multi-objective mathematical solution procedure, known as the epsilon-constraint method, are compared on several benchmark RMMRAP instances. The results obtained show the relative dominance of the proposed customized NSGA-II over the epsilon-constraint method.

Using NSGA II Algorithm for a Three Objectives Redundancy Allocation Problem with k-out-of-n Sub-Systems

In modern production systems, finding a way to improve the product and system reliability in design is very important. The reliability of the products and systems may improve using different methods. One of these methods is redundancy allocation problem. In this problem, by adding redundant components to sub-systems under some constraints, the reliability would improve. In this paper, we worked on a three-objective redundancy allocation problem. The objectives are maximizing system reliability and minimizing the system cost and weight. The structure of sub-systems are k-out-of-n and the components have constant failure rate. Because this problem belongs to “Np. Hard problems”, we used NSGA II multi-objective Meta-heuristic algorithm to solve the presented problem.

An open-source optimization tool for solar home systems: A case study in Namibia

Solar home systems (SHSs) represent a viable technical solution for providing electricity to households and improving standard of living conditions in areas not reached by the national grid or local grids. For this reason, several rural electrification programmes in developing countries, including Namibia, have been relying on SHSs to electrify rural off-grid communities. However, the limited technical know-how of service providers, often resulting in over- or under-sized SHSs, is an issue that has to be solved to avoid dissatisfaction of SHSs’ users. The solution presented here is to develop an open-source software that service providers can use to optimally design SHSs components based on the specific electricity requirements of the end-user.The aim of this study is to develop and validate an optimization model written in MS Excel-VBA which calculates the optimal SHSs components capacities guaranteeing the minimum costs and the maximum system reliability. The results obtained with the developed tool showed good agreement with a commercial software and a computational code used in research activities. When applying the developed optimization tool to existing systems, the results identified that several components were incorrectly sized. The tool has thus the potentials of improving future SHSs installations, contributing to increasing satisfaction of end-users.