Redundancy Allocation for Reliability Design of Engineering Systems With Failure Interactions

2015 ◽  
Vol 137 (3) ◽  
Author(s):  
Jing Wang ◽  
Mian Li
Keyword(s):  
Analytical Model ◽  
System Reliability ◽  
Optimal Allocation ◽  
Interaction Model ◽  
Failure Rates ◽  
Engineering Systems ◽  
Redundancy Allocation ◽  
Reliability Design ◽  
Allocation Problems ◽  
Failure Interaction

Optimal allocation of redundancies is one of the most important ways of improving system reliability. Generally, in these redundancy allocation problems, it is assumed that failures of components are independent. However, under this assumption failure rates can be underestimated since failure interactions can significantly affect the performance of systems. In this paper, we first propose an analytical model to describe the failure rates with failure interactions, followed by a modified analytical hierarchy process (MAHP) which is proposed to solve redundancy allocation problems with failure interactions. MAHP decomposes the system into several blocks and deals with those downsized blocks before diving deep into the most appropriate component for redundancy allocation. Being simple and flexible, MAHP provides an intuitive way to design a complex system and the explicit function of the entire system reliability is not required in the proposed approach. More importantly, with the help of the proposed analytical failure interaction model, MAHP can capture the effect of failure interactions. Results from case studies clearly demonstrate the applicability of the analytical model for failure interactions and MAHP for reliability design.

Redundancy Allocation for Reliability Design of Engineering Systems With Failure Interactions

2014 ◽  
Author(s):  
Jing Wang ◽  
Mian Li
Keyword(s):  
Analytical Model ◽  
Interaction Model ◽  
Optimization Methods ◽  
Failure Rates ◽  
Engineering Systems ◽  
Analytic Hierarchy ◽  
Redundancy Allocation ◽  
Reliability Design ◽  
Allocation Problems ◽  
Failure Interaction

In reliability design, allocating redundancy through various optimization methods is one of the important ways to improve the system reliability. Generally, in these redundancy allocation problems, it is assumed that failures of components are independent. However, under this assumption failure rates can be underestimated since failure interactions can significantly affect the performance of systems. This paper first proposed an analytical model describing the failure rates with failure interactions. Then a Modified Analytic Hierarchy Process (MAHP) is proposed to solve the redundancy allocation problems for systems with failure interactions. This method decomposes the system into several blocks and deals with those down-sized blocks before diving deep into the most appropriate component for redundancy allocation. Being simple and flexible, MAHP provides an intuitive way to design a complex system and complex explicit objective functions for the entire system is not required in the proposed approach. More importantly, with the help of the proposed analytical failure interaction model, MAHP can capture the effect of failure interactions. Results from case studies clearly demonstrate the applicability of the analytical model for failure interactions and MAHP for reliability design.

Redundancy Allocation Optimization for Multistate Systems With Failure Interactions Using Semi-Markov Process

2015 ◽  
Vol 137 (10) ◽  
Author(s):  
Jing Wang ◽  
Mian Li
Keyword(s):  
Markov Process ◽  
System Analysis ◽  
Research Work ◽  
Process Models ◽  
Redundancy Allocation ◽  
Allocation Problems ◽  
Multistate Systems ◽  
Semi Markov Process ◽  
Failure Interaction ◽  
Main Subsystem

Failure interactions and multiple states are two common phenomena in engineering systems. However, most of the redundancy allocation problems assume binary states and ignore failure interactions, which will cause inaccurate and misleading results. Although some research work focuses on the multistate systems, failure interactions have been ignored. This paper, for the first time, solves the redundancy allocation problems considering the systems having both multiple states and failure interactions. The system studied in this paper is a kind of multistate system containing a main subsystem and an auxiliary subsystem with the failure interaction existing from the auxiliary subsystem to the main subsystem. Semi-Markov process is proposed as the model for the system analysis, and a reliability measure, availability, is obtained based on the proposed semi-Markov process models. The system availability is used as the constraint in the redundancy allocation problem. A case study from a navy application is presented to demonstrate the applicability of the proposed method.

scholarly journals A two-phase linear programming approach for redundancy allocation problems

2002 ◽  
Vol 12 (2) ◽  
pp. 227-236 ◽  
Author(s):  
Yi-Chih Hsieh
Keyword(s):  
Linear Programming ◽  
System Reliability ◽  
Linear Constraints ◽  
Programming Approach ◽  
Second Phase ◽  
Two Phase ◽  
Redundancy Allocation ◽  
Linear Programming Approach ◽  
Allocation Problems ◽  
Effectiveness And Efficiency

Provision of redundant components in parallel is an efficient way to increase the system reliability, however, the weight, volume and cost of the system will increase simultaneously. This paper proposes a new two-phase linear programming approach for solving the nonlinear redundancy allocation problems subject to multiple linear constraints. The first phase is used to approximately allocate the resource by using a general linear programming, while the second phase is used to re-allocate the slacks of resource by using a 0-1 integer linear programming. Numerical results demonstrate the effectiveness and efficiency of the proposed approach.

scholarly journals Solving Cold-Standby Reliability-Redundancy Allocation Problems with Particle-Based Simplified Swarm Optimization

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Chia-Ling Huang ◽  
Yunzhi Jiang ◽  
Wei-Chang Yeh
Keyword(s):  
Particle Swarm Optimization ◽  
System Reliability ◽  
State Of The Art ◽  
Series System ◽  
Swarm Optimization ◽  
Redundancy Allocation ◽  
System A ◽  
Cold Standby ◽  
Allocation Problems ◽  
Intelligence Technique

Particle swarm optimization (PSO) and simplified swarm optimization (SSO) are two of the state-of-the-art swarm intelligence technique that is widely utilized for optimization purposes. This paper describes a particle-based simplified swarm optimization (PSSO) procedure which combines the update mechanisms (UMs) of PSO and SSO to determine optimal system reliability for reliability-redundancy allocation problems (RRAPs) with cold-standby strategy while aimed at maximizing the system reliability. With comprehensive experimental test on the typical and famous four benchmarks of RRAP, PSSO is compared with other recently introduced algorithms in four different widely used systems, i.e., a series system, a series-parallel system, a complex (bridge) system, and an overspeed protection system for a gas turbine. Finally, the results of the experiments demonstrate that the PSSO can effectively solve the system of RRAP with cold-standby strategy and has good performance in the system reliability obtained although the best system reliability is not obtained in all four benchmarks.

Redundancy allocation problem in a bridge system with dependent subsystems

2018 ◽  
Vol 233 (4) ◽  
pp. 658-669
Author(s):  
Kamyar Sabri-Laghaie ◽  
Milad Eshkevari ◽  
Mahdi Fathi ◽  
Enrico Zio
Keyword(s):  
System Reliability ◽  
Allocation Problem ◽  
Bridge Structure ◽  
Swarm Optimization ◽  
Redundancy Allocation Problem ◽  
Redundancy Allocation ◽  
Reliability Design ◽  
Copula Theory ◽  
Real World Applications ◽  
System Configurations

The redundancy allocation problem is an important problem in system reliability design. Many researchers have investigated the redundancy allocation problem under different assumptions and for various system configurations. However, most of the studies have disregarded the dependence among components and subsystems. In real-world applications, the performance of components and subsystems can affect each others. For instance, the heat radiated by a subsystem can accelerate degradation of adjacent components or subsystems. In this article, a procedure is proposed for solving the redundancy allocation problem of a bridge structure with dependent subsystems. Copula theory is utilized for modeling dependence among subsystems, and artificial neural network and particle swarm optimization are applied for finding the best redundancy allocation. A numerical example is included to elaborate the proposed procedure and show its applicability.

scholarly journals MILP Models for Complex System Reliability Redundancy Allocation with Mixed Components

2020 ◽  
Vol 32 (3) ◽  
pp. 600-619
Author(s):  
Young Woong Park
Keyword(s):  
Complex System ◽  
Sample Size ◽  
System Reliability ◽  
Optimal Allocation ◽  
Resource Constraints ◽  
Optimal Solution ◽  
Allocation Problem ◽  
Mixed Integer ◽  
Redundancy Allocation Problem ◽  
Redundancy Allocation

The redundancy allocation problem (RAP) aims to find an optimal allocation of redundant components subject to resource constraints. In this paper, mixed integer linear programming (MILP) models and MILP-based algorithms are proposed for complex system reliability redundancy allocation problem with mixed components, where the system have bridges or interconnecting subsystems and each subsystem can have mixed types of components. Unlike the other algorithms in the literature, the proposed MILP models view the problem from a different point of view and approximate the nonconvex nonlinear system reliability function of a complex system using random samples. The solution to the MILP converges to the optimal solution of the original problem as sample size increases. In addition, data aggregation-based algorithms are proposed to improve the solution time and quality based on the proposed MILP models. A computational experiment shows that the proposed models and algorithms converge to the optimal or best-known solution as sample size increases. The proposed algorithms outperform popular metaheuristic algorithms in the literature.

Optimal element allocation and sequencing of multi-state series systems with two levels of performance sharing

2020 ◽  
pp. 1748006X2094784
Author(s):  
Hui Xiao ◽  
Minhao Cao ◽  
Gang Kou ◽  
Xiaojun Yuan
Keyword(s):  
System Reliability ◽  
Optimization Problem ◽  
Optimal Allocation ◽  
Reliability Evaluation ◽  
Function Technique ◽  
Engineering Systems ◽  
Evaluation Algorithm ◽  
Complex Engineering ◽  
Series Systems ◽  
Complex Engineering Systems

Motivated by real-world complex engineering systems, this research proposes a multi-state system with two levels of performance sharing. In this system, there exists a unique performance transmitter between any two adjacent subsystems and any two adjacent elements within each subsystem. Surplus performance of a subsystem or an element can only be transmitted to its adjacent subsystems or elements. We build the reliability model of the proposed system and suggest a corresponding reliability evaluation algorithm by extending the existing universal generating function technique. Since the performance sharing is only allowed between adjacent subsystems and elements, the element allocation and sequencing will affect the system reliability. Due to the complexity of the combinatorial optimization problem, we use the genetic algorithm to find the optimal allocation and sequencing of the elements. An analytical example is provided to illustrate the reliability evaluation algorithm. Numerical experiments are carried out to demonstrate how the optimal allocation and sequencing as well as the capacities of the performance transmitters affect the system reliability.

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