Reliability-BasedModel for Incomplete Preventive ReplacementMaintenance of Photovoltaic Power Systems

At present,the operation and maintenance of photovoltaic power generation systems mainly comprise regular maintenance,breakdown maintenance,and condition-based maintenance,which is very likely to lead to over-or under-repair of equipment.Therefore,a preventive maintenance and replacement strategy for PV power generation systems based on reliability as a constraint is proposed.First,a hybrid failure function with a decreasing service age factor and an increasing failure rate factor is introduced to describe the deterioration of PV power generation equipment,and the equipment is replaced when its reliability drops to the replacement threshold in the last cycle.Then,based on the reliability as a constraint,the average maintenance cost and availability of the equipment are considered,and the non-periodic incomplete maintenance model of the PV power generation system is established to obtain the optimal number of repairs,each maintenance cycle and the replacement cycle of the PV power generation system components.Next,the inverter of a PV power plant is used as a research object.The model in this paper is compared and analyzed with the equal cycle maintenance model without considering reliability and the maintenance model without considering the equipment replacement threshold,Through model comparison,when the optimal maintenance strategy is(0.80,4),the average maintenance cost of this paper’s model are decreased by 20.3%and 5.54%and the availability is increased by 0.2395% and 0.0337%,respectively,compared with the equal-cycle maintenance model without considering the reliability constraint and the maintenance model without considering the equipment replacement threshold.Therefore,this maintenance model can ensure the high reliability of PV plant operation while increasing the equipment availability to improve the system economy.

Component reallocation and system replacement maintenance based on availability and cost in series systems

Component reallocation(CR)is receiving increasing attention in many engineering systems with functionally interchangeable and unbalanced degradation components.This paper studies a CR and system replacement maintenance policy of series repairable systems,which undergoes minimal repairs for each emergency failure of components,and considers constant downtime and cost of minimal repair,CR and system replacement.Two binary mixed integer nonlinear programming models are respectively established to determine the assignment of CR,and the uptime right before CR and system replacement with the objective of minimizing the system average maintenance cost and maximizing the system availability.Further,we derive the optimal uptime right before system replacement with maximization of the system availability,and then give the relationship between the system availability and the component failure rate.Finally,numerical examples show that the CR and system replacement maintenance policy can effectively reduce the system average maintenance cost and improve the system availability,and further give the sensitivity analysis and insights of the CR and system replacement maintenance policy.

Opportunistic maintenance for multi-component systems considering structural dependence and economic dependence

Although opportunistic maintenance strategies are widely used for multi-component systems, all opportunistic mainte- nance strategies only consider economic dependence and do not take structural dependence into account. An opportunistic main- tenance strategy is presented for a multi-component system that considers both structural dependence and economic dependence. The cost relation and time relation among components based on structural dependence are developed. The maintenance strategy for each component of a multi-component system involves one of five maintenance actions, namely, no-maintenance, a minimal maintenance action, an imperfect maintenance action, a perfect maintenance action, and a replacement action. The maintenance action is determined by the virtual age of the component, the life expectancy of the component, and the age threshold values. Monte Carlo simulation is designed to obtain the optimal oppor- tunistic maintenance strategy of the system over its lifetime. The simulation result reveals that the minimum maintenance cost with a strategy that considers structural dependence is less than that with a strategy that does not consider structural dependence. The availability with a strategy that considers structural dependence is greater than that with a strategy that does not consider structural dependence under the same conditions.

Condition-based maintenance optimization for continuously monitored degrading systems under imperfect maintenance actions

Condition-based maintenance(CBM) is receiving increasing attention in various engineering systems because of its effectiveness. This paper formulates a new CBM optimization problem for continuously monitored degrading systems considering imperfect maintenance actions. In terms of maintenance actions,in practice, they scarcely restore the system to an as-good-as new state due to residual damage. According to up-to-data researches, imperfect maintenance actions are likely to speed up the degradation process. Regarding the developed CBM optimization strategy, it can balance the maintenance cost and the availability by the searching the optimal preventive maintenance threshold.The maximum number of maintenance is also considered, which is regarded as an availability constraint in the CBM optimization problem. A numerical example is introduced, and experimental results can demonstrate the novelty, feasibility and flexibility of the proposed CBM optimization strategy.

Statistical approach to determination of overhaul and maintenance cost of loading equipment in surface mining

The purpose of this research was to develop a new approach in determination of overhaul and maintenance cost of loading equipment in surface mining. Two statistical models including univariate exponential regression (UER) and multivariate linear regression (MLR) were used in this study. Loading equipment parameters such as bucket capacity, machine weight, engine power, boom length, digging depth, and dumping height were considered as variables. The results obtained by models and mean absolute error rate indicate that these models can be applied as the useful tool in determination of overhaul and maintenance cost of loading equipment. The results of this study can be used by the decision-makers for the specific surface mining operations.

Multicomponent opportunistic maintenance model based on cost-effectiveness

With difficulties in maintaining multicomponent systems of wind turbines and formulating economical and reasonable maintenance strategies,a dynamic opportunistic maintenance strategy of multicomponent systems is applied in terms of economic relevance and opportunistic maintenance among various components.A preventive maintenance model based on cost-effectiveness is proposed by incorporating cost-effectiveness analysis into the multicomponent preventive maintenance strategy.The failure rate recovery degree is used to describe the effects of imperfect maintenance and replacement.When the reliability of the component reaches the threshold of preventive or opportunistic maintenance,a reasonable maintenance method is selected on the basis of the cost-effectiveness ratio of the failure rate.A case study is conducted by taking four components of a wind turbine as the research object and comparing them with the opportunistic maintenance model without considering cost-effectiveness.Results show that the total maintenance cost is reduced by 373600 yuan,indicating that the preventive opportunistic maintenance based on cost-effectiveness is more economical and can provide a theoretical basis for formulating a preventive maintenance plan.

Prediction of Repair and Maintenance Costs of Two-wheel Drive Tractors in Iran

Predicting of repair and maintenance （R＆M） costs of tractors in any mechanized farm is necessary for owners and managers to obtain information on overall costs and to control financial balance and production economy. In this context a study was conducted to predict accumulated R＆M costs （Y） of two-wheel drive （2WD） tractors based on accumulated usage hours （X） in Misagh-e-Sabz Agribusiness Company in Iran. Recorded data of the company were used to determine regression models for predicting accumulated R＆M costs （as percentage of initial purchase price） based on accumulated usage hours. The statistical results of the study indicated that in order to predict accumulated R＆M costs of 2WD tractors with accumulated usage hours of 2260 h or less the power regression model Y=0.013 （X/100）1.677 with R2=0.976, and to predict accumulated R＆M costs of 2WD tractors with accumulated usage hours of 2260 h or more the polynomial regression model Y=0.004 （X/100）2-0.181 （X/1 00）＋4.373 with R2=0.998 can be strongly suggested.

A condition-based maintenance policy for reconfigurable multi-device systems

The exploration of component states for optimizing maintenance schedules in complex systems has garnered significant interest from researchers.However,current literature usually overlooks the critical aspects of system flexibility and reconfigurability.Judicious implementation of system reconfiguration can effectively mitigate system downtime and enhance production continuity.This study proposes a dynamic condition-based maintenance policy considering reconfiguration for reconfigurable systems.A double-layer decision rule was constructed for the devices and systems.To achieve the best overall maintenance effect of the system,the remaining useful life probability distribution and recommended maintenance time of each device were used to optimize the concurrent maintenance time window of the devices and determine whether to reconfigure them.A comprehensive maintenance efficiency index was introduced that simultaneously considered the maintenance cost rate,reliability,and availability of the system to characterize the overall maintenance effect.The reconfiguration cost was included in the maintenance cost.The proposed policy was tested through numerical experiments and compared with different-level policies.The results show that the proposed policy can significantly reduce the downtime and maintenance costs and improve the overall system reliability and availability.

New Maintenance Strategies for Generating Companies in Power Market

A new approach to maintenance scheduling of generating units(MSU)in competitive electricity markets was presented,which was formulated as a noncooperative game with complete information.The payoff of each generating company(Genco)was defined as the profit from the energy auction market minus maintenance cost and risk loss.The compensation fee of interruptible load was a part of the maintenance cost when the permitted maintenance capacity in the system was insufficient.Hourly energy auction was incorporated in the computation of both revenues from energy market and risk loss of maintenance strategy as a nested game.A new heuristic search algorithm for the calculation of the game equilibrium of MSU was presented,which coordinates the solutions of non-equilibrium,unique equilibrium and multiple equilibria.Numerical results for a two-Genco system and a realistic system were used to demonstrate the basic ideas and the applicability of the proposed method,as well as its computational efficiency.

An Investigation into the Relationship Between Maintenance and Finance

One of the prime objectives of maintenance is to minimize the total maintenance costs. Total maintenance costs include direct costs and maintenance dependent costs. Direct costs include labor, material, and overheads. Whereas maintenance dependent costs refer to the costs of lost production or loss of revenue due to breakdown maintenance.

Optimization of Aeroengine Shop Visit Cost in its Service Life Cycle

It is an important task for airlines to make a reasonable workscope for their engines, which has effects not only on engine performance and reliability, but also on airlines operating cost. Based on the recommendations given in the engine maintenance management manual, and taking the repair levels adopted in the previous shop visits into account, a series of module repair level optimization rules were set up, and a shop visit cost optimization model was also created for engine service life cycle. The particle swarm method was used to optimize the engine workscope and overhaul cost. The method proposed in this paper will be a reference for airlines to make engine workscope and to do engine maintenance management.

An Analysis of the Influence of Equipment Reliability on M&S Cost in Multi-theater Deployments

This paper theoretically analyzes the influence of weapon reltability on maintenance and support cost （ M＆S cost） in multi-theater deployments. Based on the M＆S data of typical on-board equipment, it analyzes in depth the M＆S cost in a three-theater deployment scenario with PRICE software and the influence of reliability on M＆S cost. The result shows that under the same hardware parameter index, the M＆S cost in multi-theater deployments is much higher than that in one-theater deployments and that the cost of equipment Mean Time Between Failures （ MTBF） is also more sensitive than that in one-theater deployments, particularly when the MTBF is relatively low. As a result, the requirement for equipment reliability is higher in multi-theater deployments.

Artificial neural network-based repair and maintenance cost estimation model for rice combine harvesters

This research proposes an artificial neural network(ANN)-based repair and maintenance(R&M)cost estimation model for agricultural machinery.The proposed ANN model can achieve high estimation accuracy with small data requirement.In the study,the proposed ANN model is implemented to estimate the R&M costs using a sample of locally-made rice combine harvesters.The model inputs are geographical regions,harvest area,and curve fitting coefficients related to historical cost data;and the ANN output is the estimated R&M cost.Multilayer feed-forward is adopted as the processing algorithm and Levenberg-Marquardt backpropagation learning as the training algorithm.The R&M costs are estimated using the ANN-based model,and results are compared with those of conventional mathematical estimation model.The results reveal that the percentage error between the conventional and ANN-based estimation models is below 1%,indicating the proposed ANN model’s high predictive accuracy.The proposed ANN-based model is useful for setting the service rates of agricultural machinery,given the significance of R&M cost in profitability.The novelty of this research lies in the use of curve-fitting coefficients in the ANN-based estimation model to improve estimation accuracy.Besides,the proposed ANN model could be further developed into web-based applications using a programming language to enable ease of use and greater user accessibility.Moreover,with minor modifications,the ANN estimation model is also applicable to other geographical areas and tractors or combine harvesters of different countries of origin.

Optimization of post-warranty sequential inspection for second-hand products

This paper considers an optimal sequential inspection schedule for a second-hand product after that the free nonrenewable warranty is expired. The length of warranty is prespecified and during the warranty period, the product is minimally repaired by the dealer when it fails. Following the expiration of the non-renewing warranty, the product is inspected and upgraded sequentially a fixed number of times at the expenses of the customer.At each inspection, the failure rate of the product is reduced proportionally so that the product is upgraded. The product is assumed to deteriorate as it ages and the replacement of the product occurs when a fixed number of inspections are rendered. In addition,the intervals between two successive inspections are assumed to decrease monotonically. The main objective of this paper is to determine the optimal improvement level to upgrade the product at each inspection so that the expected maintenance cost during the life cycle of the product is minimized from the perspective of the customer. Under the given cost structures, we derive an explicit formula to obtain the expected maintenance cost incurred during the life cycle of the product and discuss the method to find the optimal level of the improvement analytically in case the failure times follow the Weibull distribution. Numerical results are analyzed to observe the impact of relevant parameters on the optimal solution.

Bidirectional Clone Node Model of Optimizing Performance of Structured P2P Overlay Network

In order to reduce the maintenance cost of structured Peer-to-Peer (P2P),Clone Node Protocol (CNP) based on user behavior is proposed.CNP considers the regularity of user behavior and uses the method of clone node.A Bidirectional Clone Node Chord model (BCNChord) based on CNP protocol is designed and realized.In BCNChord,Anticlockwise Searching Algorithm,Difference Push Synchronize Algorithm and Optimal Maintenance Algorithm are put forward to increase the performances.In experiments,according to the frequency of nodes,the maintenance cost of BCNChord can be 3.5%～32.5% lower than that of Chord.In the network of 212 nodes,the logic path hop is steady at 6,which is much more prior to 12 of Chord and 10 of CNChord.Theoretical analysis and experimental results show that BCNChord can effectively reduce the maintenance cost of its structure and simultaneously improve the query efficiency up to (1/4)O(logN).BCNChord is more suitable for highly dynamic environment and higher real-time system.

Reliability-Based Full-Life Cycle Optimum Design of Offshore Jacket Platform

Based on the consideration of operation environment and structural property, an optimum design model of offshore jacket platform is developed in this paper, namely, the reliability-based full-life cycle optimum design model. In this model, the time-dependent reliability assessment method for structural members is established by combination of the decrease of sectional size and performance deterioration of material. The initial investment, maintenance cost and failure loss cost are assembled into the model. The total cost of the platform structure system in its full service period is chosen as the objective function, and the initial reliabilities of the layer elements partitioned in advance are taken as the design variables. Different models are obtained, depending on whether the system reliability constraint is considered or not. This optimum design model can result in the lowest full-life cost and the optimal initial layer reliability of an offshore jacket platform in the design of marine structures. The feasibility of this model is illustrated with an actual jacket platform in the Liaodong Gulf as an example.

Strategy-Based Forecasting Model for Civil Airlines

Airlines usually pay more attention to maintenance cost for efficiency improvement and consumption reduction. However, airlines, especially the domestic airlines, can hardly predict the cost exactly due to the uncertainty and complexity until now. In practice, the cost is calculated by collecting and calculating the invoices afterwards. To settle the problem, a maintenance cost forecasting model is proposed in this paper. Maintenance activities are classified into scheduled maintenance and unscheduled maintenance. Scheduled maintenance is periodic, in which the required materials and man-power hours can be obtained properly in advance. Nevertheless, it is impossible to acquire the necessary information of unscheduled maintenance. According to the specific characteristics of each, Activity-Based Costing (ABC) and Cost Estimating Relationships (CERs) are introduced to attack the building of forecasting models, respectively. Then practical cases, the 3C check of MD-90 and the engine shop visit are adopted to verify the cost forecasting models proposed. The results show that the models not only can predict the actual maintenance cost successfully, but also are helpful to drawing up the maintenance program and managing the maintenance funds efficiently.

Security Analysis Model, System Architecture and Relational Model of Enterprise Cloud Services

Cloud computing is one of the fastest growing and popular computer technologies, and there are more and more enterprise services based on the cloud computing. In order to save costs, more and more enterprises and their employees have hired the enterprise cloud services, and put much important information in the cloud gradually. Cloud service systems have become the main targets of malicious attacks. However, the cloud computing technologies are still not perfect, and the management and maintenance of enterprise cloud services are more complex compared to traditional network services of cloud computing. So, enterprise cloud services are more likely to encounter some security problems, and the influenced scale of these security problems is broad. But there are few researches on the security of enterprise cloud services. In this paper, we analyze the software as a service（Saa S） enterprise cloud services and introduce the research status of security problems in cloud computing environment. Combining with the analysis of the characteristics and application architecture of Saa S enterprise cloud services, we propose the security problems analysis model, the analysis system architecture and the relational model. Our researches can support further research of the automatic generation of solutions and guide the deployment of security policies of Saa S enterprise cloud services.

A Method of Minimum Reusability Estimation for Automated Software Testing

Through reusing software test components, automated software testing generally costs less than manual software testing. There has been much research on how to develop the reusable test components, but few fall on how to estimate the reusability of test conlponents for automated testing. The purpose of this paper is to present a method of minimum reusability estimation for automated testing based on the return on investment （ROI） model. Minimum reusability is a benchmark for the whole automated testing process. If the reusability in one test execution is less than the minimum reusability, some new strategies must be adopted ill the next test execution to increase the reusability. Only by this way, we can reduce unnecessary costs and finally get a return on the investment of automated testing.