Series-parallel System Research Articles

Joint optimization of condition-based maintenance and spares inventory for a series–parallel system with two failure modes

In the field of industrial engineering, the joint optimization of maintenance and spares inventory has attracted more and more attention because it can better balance system availability and cost. However existing studies are usually restricted to a single failure mode and a simple maintenance strategy of the single component systems. To bridge these gaps, this paper investigates the joint optimization of condition-based maintenance and spares inventory for a general series–parallel system with two failure modes. Hard failures are self-announcing, and soft failures are generally caused by the degradation of components and only be discovered through inspection. At the time of each periodic inspection, the corresponding corrective maintenance, preventive maintenance and spare parts ordering policy are determined. Upon a hard failure occurs, an opportunistic inspection will be performed, and it will be determined whether to perform maintenance actions based on the degradation level of the components and spares inventory level. Furthermore, the state transition probability and the expected sojourn time can be derived by the formulated semi-Markov decision process. To minimize the expected average cost per unit time, the optimal preventive maintenance and the spares inventory control policies are jointly determined by applying a simulation method. Finally, a numerical experiment is presented to demonstrate the effectiveness and superiority of the proposed joint optimization model.

Optimal selective maintenance scheduling for series–parallel systems based on energy efficiency optimization

With the industry sustainable development and increasing awareness of energy conservation, many manufacturing enterprises prefer to develop the operation and maintenance (O&M) with high production throughput and low energy consumption. In reality, many manufacturing systems are required to conduct a sequence of predefined missions with finite breaks between any two consecutive missions. To successfully complete the next mission production, maintenance actions are arranged and performed on machines during each scheduled break. In this paper, an energy-oriented selective maintenance policy (ESMP) for series–parallel systems is investigated. At each break, multiple maintenance actions with different impacts on machine degradation are available under limited maintenance resources. To obtain the throughput-and-energy based maintenance scheme, we first model the system energy efficiency based on system throughput and energy consumption. Then, we integer the energy efficiency modeling, production throughout analysis, and selective maintenance scheduling into an optimization model. And the model objective is to find the appropriate maintenance action for each machine at each break subject to cost and duration constraints. Numerical examples have been addressed to demonstrate the performance and adaptability of our proposed ESMP in long-term selective maintenance scheduling. Finally, a comparative analysis with traditional reliability-oriented policy shows the significant improvement of energy efficiency.

Performance Assessment and Sensitivity Analysis of a Computer Lab Network Through Copula Repair with Catastrophic Failure

The advent of copula distribution by Gumhel-Hougaard family spurred a new direction of research in multi-state complex engineering systems and is widely applied in various series-parallel systems. Considering this aspect, in this paper we study various reliability measures of a complex system consisting of eight identical computer labs as star topology working under 5-out-of-8: G policy, two different centralized data base servers working under 1-out-of-2: G policy, and a switch in series configuration. Failure rates of all the units are assumed to be constant and follow exponential distribution, while repair supports general distribution and copula distribution. The objective of this paper is to evaluate availability of the system, reliability of the system, mean time to failure and expected profit analysis by choosing arbitrary values of the parameters in a way that numerical solutions can be obtained systematically in a reasonable computational time. The problem is modelled using supplementary variable technique, Laplace transform and copula repair. We highlight the use of copula repair, while identifying the factors for improvement and future directions of work.

Reliability Uncertainty Analysis Method for Aircraft Electrical Power System Design Based on Variance Decomposition

As a safety critical system, affected by cognitive uncertainty and flight environment variability, aircraft electrical power system proves highly uncertain in its failure occurrence and consequences. However, there are few studies on how to reduce the uncertainty in the system design stage, which is of great significance for shortening the development cycle and ensuring flight safety during the operation phrase. For this reason, based on the variance decomposition theory, this paper proposes an importance measure index of the influence of component failure rate uncertainty on the uncertainty of power supply reliability (system reliability). Furthermore, an algorithm to calculate the measure index is proposed by combining with the minimum path set and Monte Carlo simulation method. Finally, the proposed method is applied to a typical series-parallel system and an aircraft electrical power system, and a criteria named as “quantity and degree optimization criteria” is drawn from the case study. Results demonstrate that the proposed method indeed realizes the measurement of the contribution degree of component failure rate uncertainty to system reliability uncertainty, and combined with the criteria, proper solutions can be quickly determined to reduce system reliability uncertainty, which can be a theoretical guidance for aircraft electrical power system reliability design.

Maintenance cost-based importance analysis under different maintenance strategies

As system components and their interactions become more and more complex, system reliability problems become increasingly prominent. In the reliability research, component importance measures have been widely used as an important decision-making aid index. Particularly, importance measures have been used in designing or selecting the optimal maintenance strategy. However, the practical factor of maintenance cost is not comprehensively considered in the existing importance analysis. This paper proposes new importance measures based on two types of maintenance costs under different maintenance strategies, including the failure-triggered replacement strategy that replaces damaged components, the age-based replacement strategy where a component is preventively replaced and when it has been used for a certain period of time, and the hybrid strategy that integrates the former two strategies. Methods for computing the proposed maintenance cost-based importance measures for series systems and parallel systems are given. A case study of a propeller plane system is provided to illustrate and validate the proposed method.

Collection System Topology for Deep-Sea Offshore Wind Farms Considering Wind Characteristics

DC series-parallel collection system has recently been widely discussed for integrating deep-sea OWFs because it saves the offshore HVDC stations. However, comparing with traditional offshore electrical collection systems, there is a unique operational problem introducing by the DC series-parallel system that is the wind power curtailment caused by the wind power fluctuations. In this paper, a rigorous mathematical analysis of wind power curtailment is conducted, and an optimization model considering wind power curtailment for DC series-parallel collection systems is proposed. To address the particularities of the series-parallel collection system in graph theory, an improved parthenogenetic algorithm (PGA) is suggested to address the optimization problem. The proposed model and optimization algorithms are tested based on a deep-sea OWF with 56 wind turbines (WTs). The simulation results show that considering wind power curtailment during the DC series-parallel system planning helps to save the levelized cable cost of energy (LCCOE) in the long term, and putting the wiring direction of the series as perpendicular as possible to the prevailing wind direction of the OWF helps to increase the power generation of the OWF and reduce LCCOE.

Optimizing a joint reliability-redundancy allocation problem with common cause multi-state failures using immune algorithm

Redundancy-reliability allocation problem (RRAP) is a well-known problem in reliability area. In general, this problem aims to maximize a system’s reliability or minimize a system’s costs under some constraints. In this paper, we develop a RRAP for a series-parallel system with multi-state components. Thus, the subsystems’ components, the system’s subsystems, and the system have different working states with corresponding working probabilities. The RAP in the paper is a RAP with mix components (RAPMC). We consider the choice of allocating non-identical components to each sub-system. Moreover, we consider the common cause failure (CCF) for the components, which causes simultaneous failure of all identical components of a subsystem. We assume the component’s failure state probability is reduced by conducting technical activities, and the reduced probability is added to the component’s working states’ probabilities. The model’s objective function is to minimize the system’s costs under a minimum reliability level and other constraints by allocating the optimal set of components to each subsystem and determining each component’s technical activities level. Since the RRAP belongs to the Np-Hard category of problems, an immune algorithm is used to solve the developed problem. The results indicate considering the technical activities decreases the system’s costs.

A hybrid salp swarm algorithm based on TLBO for reliability redundancy allocation problems.

A novel optimization algorithm called hybrid salp swarm algorithm with teaching-learning based optimization (HSSATLBO) is proposed in this paper to solve reliability redundancy allocation problems (RRAP) with nonlinear resource constraints. Salp swarm algorithm (SSA) is one of the newest meta-heuristic algorithms which mimic the swarming behaviour of salps. It is an efficient swarm optimization technique that has been used to solve various kinds of complex optimization problems. However, SSA suffers a slow convergence rate due to its poor exploitation ability. In view of this inadequacy and resulting in a better balance between exploration and exploitation, the proposed hybrid method HSSATLBO has been developed where the searching procedures of SSA are renovated based on the TLBO algorithm. The good global search ability of SSA and fast convergence of TLBO help to maximize the system reliability through the choices of redundancy and component reliability. The performance of the proposed HSSATLBO algorithm has been demonstrated by seven well-known benchmark problems related to reliability optimization that includes series system, complex (bridge) system, series-parallel system, overspeed protection system, convex system, mixed series-parallel system, and large-scale system with dimensions 36, 38, 40, 42 and 50. After illustration, the outcomes of the proposed HSSATLBO are compared with several recently developed competitive meta-heuristic algorithms and also with three improved variants of SSA. Additionally, the HSSATLBO results are statistically investigated with the wilcoxon sign-rank test and multiple comparison test to show the significance of the results. The experimental results suggest that HSSATLBO significantly outperforms other algorithms and has become a remarkable and promising tool for solving RRAP.

Techno-economic-environmental evaluation of a combined tri and dry reforming of methane for methanol synthesis with a high efficiency CO2 utilization

Production of methanol, as a green energy, from syngas is coming into focus. However, natural gas based methanol plants, which are used steam reforming of methane for syngas production, have a high CO 2 emission resulting in the global warming. In this study, a novel process for methanol synthesis is proposed to reduce CO 2 emission. In this regard, natural gas and flue gas are fed to a parallel-series system with tri and dry reforming of methane for syngas production with the optimized stoichiometric number. Then, the produced syngas is converted to methanol in a reactor. Finally, the produced methanol is purified by two distillation towers. The proposed method is compared to a referenced method in the view of technological, economic and environmental metrics. The techno-economic-environmental analysis of the processes reveals that not only the proposed method, as compared to the referenced one, increases CO 2 conversion from 20.93% to 99.22%, but also it is more economical and environmentally friendly. In addition, the global warming potential of the proposed method is almost 60% lower than that for the referenced method due to the lower CO 2 emission. Therefore, the proposed method can save above MUS$ 8 a year by CO 2 capture. • Combination of tri and dry reforming of methane to produce syngas for methanol synthesis. • Utilization of flue gas, instead of pure CO 2 , as a raw material to reduce CO 2 purification cost. • Reduction of CO 2 emission of a natural gas based methanol plant. • A techno-economic-environmental analysis of the proposed and referenced processes. • Investigation of the effect of the processes on the global warming potential (GWP).

The methods for exactly solving redundancy allocation optimization for multi-state series–parallel systems

Redundancy allocation problems (RAPs) is a classical family of reliability optimization problems. RAP for multi-state systems (MSSs) is among the most difficult RAPs. Multi-state series–parallel system (MSSPS) is among the most commonly-applied structure of MSSs. To the knowledge of the authors, in literature there is no exact approach to solve MSSPS RAP. In this work, we propose an efficient exact solution method based on dynamic programming to solve it. In literature, there are two formulations of RAP, namely maximizing the system reliability and minimizing of the system cost under the resource constraints. In this work, we investigate the conversion between the two formulations such that the two different RAP formulations can be exactly solved via our proposed method. Experiments are conducted on the well-known benchmarks. The results are compared with the published ones achieved by meta-heuristics. Our methods confirm that majority of the published solutions are in fact global-optimal. In addition, our methods find three new global-optimal solutions. The experiments also illustrate that on systems with more subsystems and fewer component types, our proposed method significantly outperforms the heuristic method in MSSPS RAP with exact optimal solutions and less running time.

Multi-objective imperfect selective maintenance optimization for series-parallel systems with stochastic mission duration

To improve the probability that an engineering system successfully completes its next mission, it is crucial to implement timely maintenance activities, especially when maintenance time or maintenance resources are limited. Taking series-parallel system as the object of study, this paper develops a multi-objective imperfect selective maintenance optimization model. Among it, during the scheduled breaks, potential maintenance actions are implemented for the components, ranging from minimal repair to replacement. Considering that the level of maintenance actions is closely related to the maintenance cost, age reduction coefficient and hazard rate adjustment coefficient are taken into account. Moreover, improved hybrid hazard rate approach is adopted to describe the reliability improvement of the components, and the mission duration is regarded as a random variable. On this basis, a nonlinear stochastic optimization model is established with dual objectives to minimize the total maintenance cost and maximize the system reliability concurrently. The fast elitist non-dominated sorting genetic algorithm (NSGA-II) is adopted to solve the model. Numerical experiments are conducted to verify the effectiveness of the proposed approach. The results indicate that the proposed model can obtain better scheduling schemes for the maintenance resources, and more flexible maintenance plans are gained.

Reliability optimisation of parallel-series system with interval valued and fuzzy environment via GA

Reliability optimisation of parallel-series system with interval valued and fuzzy environment via GA

Reliability allocation and optimisation by using Kuhn-Tucker and geometric programming for series-parallel system

Reliability allocation and optimisation by using Kuhn-Tucker and geometric programming for series-parallel system

Reliability Optimization of Redundancy Allocation Problem of a Series-parallel System using NAQPSO Algorithm in Triangular Neutrosophic Environment

Reliability Optimization of Redundancy Allocation Problem of a Series-parallel System using NAQPSO Algorithm in Triangular Neutrosophic Environment

Reliability allocation and optimisation by using Kuhn-Tucker and geometric programming for series-parallel system

Reliability allocation and optimisation by using Kuhn-Tucker and geometric programming for series-parallel system

Estimation and Optimization for System Availability Under Preventive Maintenance

Engineers design systems to be reliable and work to fulfil their missions without failure for a specific period. However, the system components deteriorate with time and lead to its failures. A frequent system failure increases the management costs, hence posing a challenge to decision-makers. Therefore, for the avoidance of frequent system failures, preventive maintenance is necessary. The objective of any manufacturing firm is to maximize profit and minimize costs. The interval for preventive maintenance can be optimized if the system’s availability is maximized and its cost function minimized. This study evaluates the availability and cost function for a continuous operating series-parallel system under a fixed time environment. A multiobjective model is formulated to maximize the availability and minimize the cost function of the system. The study illustrated a numerical example and solved using goal programming (GP), fuzzy goal programming (FGP), genetic algorithm (GA), and particle swarm optimization (PSO) techniques. The results are compared using a robust statistical test and, the PSO proves to be better. A simulation study was carried out further to evaluate the availability and cost function using R and MATLAB packages.

Multi-objective availability and cost optimization by PSO and COA for series-parallel systems with subsystems failure dependencies

System availability and cost are two of the elements of system dependability. Most systems involve subsystems with failure dependencies. The failure dependencies complicate the optimization of those elements. In this paper, the multi-objective optimization problem of availability and cost is addressed for series-parallel systems with subsystems failure dependencies in case of strong dependency. The problem is solved by applying the particle swarm optimization (PSO) algorithm and the cuckoo optimization algorithm (COA). The multi-objective optimization problem is converted to a single one using the weighted-sum method. The results of a system consisting of six subsystems are analyzed with a comparison of the methods. The best value of the system availability and cost, number of function evaluations, CPU time, and standard deviation reveal that the COA has outperformed the PSO.

Redundancy strategies assessment and optimization of k-out-of-n systems based on Markov chains and genetic algorithms

The existing reliability formulation regarding mixed and K-mixed strategies considers only one component as the minimum number of required components for each subsystem. In this paper, we develop a Continuous-Time Markov Chain model for both mixed and K-mixed strategies while the minimum number of required components can be more than one and take any values. In addition, the drawbacks of the classical model, which are complicated formulation, approximate solution and time-consuming problem-solving process, have been addressed. The proposed model estimates the reliability under different redundancy strategies more efficiently and in a straightforward way. To validate the proposed approach, a sequential Monte Carlo model is also developed for the reliability analysis. Besides, the existing strategies are applied to a series-parallel system and an efficient genetic algorithm is developed to solve the resulting optimization problem. The numerical results confirm the accuracy of the Continuous-Time Markov Chain model in estimating k-out-of-n system reliability under standby, mixed and K-mixed strategies with a major reduction in the computation time.

Reliability and Performance Analysis of a Series-Parallel System Using Gumbel–Hougaard Family Copula

This research looks into the reliability metrics that are used to assess the strength of a solar system’s serial system, which is made up of four subsystems. Each subsystem consists of two parallel active components: two out of two photo-voltaic panels, one of two charge controllers, two of two batteries, and one of two inverters. Both the charge controller and the inverter have two human operators or switches. The Gumbel–Hougaard copula family was used to produce formulations of system dependability metrics such as reliability, mean time to failure (MTTF), availability, and profit function. Numerical examples are presented to show the obtained results and to investigate the impact of various system characteristics. The new study might help homes overcome some of the problems experienced by electric generation systems operating in hostile locations or under adverse weather conditions. A new model was developed, and solar photovoltaic system’s subsystems were analyzed in order to identify the most essential component. It was also indicated how to improve the system.

Estimating the parameters of a dependent model and applying it to environmental data set

In this paper, a new dependent model is introduced. The model is motivated using the structure of series-parallel systems consisting of two series-parallel systems with a random number of parallel sub-systems that have fixed components connected in series. The dependence properties of the proposed model are studied. Two estimation methods, namely the moment method, and the maximum likelihood method are applied to estimate the parameters of the distributions of the components based on observing the system's lifetime data. A Monte Carlo simulation study is used to evaluate the performance of the estimators. Two real data sets are used to illustrate the proposed method. The results are useful for researchers and practitioners interested in analyzing bivariate data related to extreme events.