Optimal Preventive Maintenance Interval Research Articles

Group Maintenance for Numerical Control Machine Tools: A Case Study

The high-reliability requirement of numerical control (NC) machine tools emphasizes the effectiveness of preventive maintenance (PM). However, most existing PM decisions are made by treating them as single-component systems, resulting in the problems of “excessive maintenance” or “insufficient maintenance.” Moreover, real failure data has rarely been utilized for maintenance decision-making, which leads to idealized policies far from practice. This article presents a framework to derive the optimal group maintenance (GM) policy from real failure histories for an NC machine tool comprised of heterogeneous components. Criticality analysis is first conducted to identify high-risk and low-risk components. The lifetime of each high-risk component is characterized by a two-parameter Weibull model or by a Weibull competing-risk model according to whether its failures are independent of the other components. High-risk components with strictly increasing or bathtub-shaped failure rates are selected for PM decision-making. This article introduces a multilevel PM decision-making approach: 1) a repair-replacement model is developed at the component level to derive the optimal PM interval and the maximum number of PM actions; and 2) at the system level, a GM procedure is presented to obtain the optimal PM interval of the system. The failure histories of an NC machine tool with 13 components are used to illustrate the effectiveness of the proposed approach. A maintenance plan is finally made based on optimization results to guide maintenance implementation.

A New Integrated Risk-Assessment Model for Minimizing Human-Machine Error Consequences in a Preventive Maintenance System

In this paper, a risk-assessment model for minimizing human-machine error consequences during the implementation of preventive maintenance tasks has developed. The developed model aimed to find the optimal preventive maintenance interval (PMI) and corresponding minimum risk consequences costs per unit of time. Based on expert judgment, a human error probability (HEP) model was developed using the success likelihood index methodology (SLIM). In addition, we developed a system failure probability (SFP) model based on the failure and repair data of the system. The HEP model was integrated with an SFP model to develop a human-machine error model. Then, the risk-assessment model was developed based on the consequences of system failure and human error. The effectiveness of the developed human-machine error model was shown by applying a numerical example for a multi-component series system. The optimization toolbox from MATLAB R2022b was applied to solve the developed human-machine model. The results showed that the optimal PMI can be implemented after 24 working hours to 105 hours under the acceptable limit of risk in the system which is 100 SR/h. Finally, the developed model is an effective method that can be applied to a wide range of manufacturing systems to minimize financial risks related to system inspection and maintenance while meeting safety and availability requirements.

Optimal preventive maintenance interval model with time value of money consideration

The search for a plant maintenance strategy to mitigate the impact of random factory machine failures has never been more challenging. Apart from the harsh global competition, manufacturing facilities have become more sophisticated and expensive; frequent and prolonged breakdowns may ruin a firm’s business. Consequently, many have turned to the Preventive Maintenance (PM) strategy for a solution. However, the efficacy of PM has been shown to depend on finding a maintenance time interval decision model capable of incorporating most of the relevant maintenance cost components. While past studies have considered maintenance system set-up, execution, and breakdown cost components, the literature is sparse on decision models incorporating the cost of borrowing money. Exploring the feasibility of developing such a model is the aim of this study. Accordingly, a maintenance-investment cost function was formulated in terms of PM time interval and plant reliability; a PM decision problem was then defined and solved as that of PM investment cost minimization. Using three cases from the literature, the optimal maintenance time intervals (238.11, 14695.28, and 2076.54), failure probabilities (0.71, 0.35, and 0.61), and costs for preventive maintenance (PM) (840950.87, 149352.55, and 33979.57) were found to be reasonable.

Optimal preventive maintenance interval model with time value of money consideration

Optimal preventive maintenance interval model with time value of money consideration

A predictive multi-objective condition-based maintenance (CBM) policy considering ecological factors

PurposeThe purpose of this paper is to propose an improved multi-objective optimization model for the condition-based maintenance (CBM) of single-component systems which considers periodic imperfect maintenance and ecological factors.Design/methodology/approachBased on the application of non-periodic preventive CBM, two recursion models are built for the system: hazard rate and the environmental degradation factor. This paper also established an optimal multi-objective model with a normalization process. The multiple-attribute value theory is used to obtain the optimal preventive maintenance (PM) interval. The simulation and sensitivity analyses are applied to obtain further rules.FindingsAn increase in the number of the occurrences could shorten the duration of a maintenance cycle. The maintenance techniques and maintenance efficiency could be improved by increasing system availability, reducing cost rate and improving degraded condition.Practical implicationsIn reality, a variety of environmental situations may occur subsequent to the operations of an advanced manufacturing system. This model could be applied in real cases to help the manufacturers better discover the optimal maintenance cycle with minimized cost and degraded condition of the environment, helping the corporations better fulfill their CSR as well.Originality/valuePrevious research on single-component condition-based predictive maintenance usually focused on the maintenance costs and availability of a system, while ignoring the possible pollution from system operations. This paper proposed a modified multi-objective optimization model considering environment influence which could more comprehensively analyze the factors affecting PM interval.

Optimization of Two-Dimensional Extended Warranty Scheme for Failure Dependence of a Multi-Component System with Improved PSO–BAS Algorithm

Human society is entering the era of Industry 4.0; engineering systems are becoming more complex, which increases the difficulties of maintenance support work. Failure dependence exists widely in multi-component systems. In this work, a model of two-dimensional (2D) warranty decision making was constructed by using a failure-dependence analysis for multi-component systems and by considering the extended warranty cost and the system availability. The decision was to cut the warranty cost as much as possible for manufacturers, while the constraint condition was the minimum acceptable availability for the customer. The model combined preventive maintenance as well as corrective maintenance strategies. Under the condition that the multi-component system is replaced upon the expiration of the extended warranty (EW), the optimal 2D EW duration and preventive maintenance interval could be obtained through the model. In a case analysis, the optimal EW scheme for the gearbox of an electric multiple unit (EMU) system was obtained by using a grid search algorithm, a PSO algorithm, and a PSO–BAS algorithm. Through comparison, the PSO–BAS algorithm obtained a better scheme with lower warranty costs and higher system availability. A comparative analysis and a sensitivity analysis showed that the model provides a theoretical basis for manufacturers to optimize their 2D extended gearbox warranties.

Optimisation of Maintenance Policies Based on Right-Censored Failure Data Using a Semi-Markovian Approach.

This paper exposes the existing problems for optimal industrial preventive maintenance intervals when decisions are made with right-censored data obtained from a network of sensors or other sources. A methodology based on the use of the z transform and a semi-Markovian approach is presented to solve these problems and obtain a much more consistent mathematical solution. This methodology is applied to a real case study of the maintenance of large marine engines of vessels dedicated to coastal surveillance in Spain to illustrate its usefulness. It is shown that the use of right-censored failure data significantly decreases the value of the optimal preventive interval calculated by the model. In addition, that optimal preventive interval increases as we consider older failure data. In sum, applying the proposed methodology, the maintenance manager can modify the preventive maintenance interval, obtaining a noticeable economic improvement. The results obtained are relevant, regardless of the number of data considered, provided that data are available with a duration of at least 75% of the value of the preventive interval.

Multi-objective optimization for preventive maintenance of offshore safety critical equipment integrating dynamic risk and maintenance cost

Safety-critical equipment (SCE) are installed on offshore installations as important safety barriers to prevent the occurrence of major accident. In order to ensure the specified safety function of SCE, preventive maintenance (PM) is an indispensable activity. However, PM decision making is still a challenge as it is influenced by various factors such as the risk in association with SCE degradation, risk in association with PM, and maintenance-related cost. Especially, the relevant risks are inherently dynamic in nature and the effects of these influence factors are conflicting. Therefore, this study focuses on the optimization of PM interval for SCE integrating the dynamic characteristics of risk and the conflicting effects among the increased risks due to PM and SCE degradation as well as maintenance-related cost. A systematic multi-objective optimization framework is proposed for the PM interval optimization. Two dynamic risk models are established for the evaluation of dynamic risk profiles of SCE in the operational phase and PM phase respectively. In combination with the lifecycle maintenance-related cost model of SCE, the multi-objective optimization with three objective functions is conducted for the PM interval optimization. The proposed framework is applied in the case study on a typical SCE (pressure safety valve, PSV) to optimize the PM interval. The effectiveness and availability of this framework is verified. It is noted that the common industrial practice of conducting PM once a year is acceptable for PSV once the risks in both operational and PM phases are given equal importance weight. However, a larger PM interval is recommended when the lifecycle maintenance-related cost is included in the optimization goal and further validation procedures are conducted, i.e. statistical analysis of optimal candidates and reassessment of risk profile with the optimal candidates. The proposed multi-optimization framework provides a useful tool for the optimal PM interval determination for the SCE on offshore installations.

Improving the availability of Australian hospitals’ critical medical devices

Maintenance of Australian hospitals’ Critical Medical Devices (CMDs) can be somewhat more demanding than conventional assets from other industries. This is primarily driven by the requirement for CMDs to operate with exceptional levels of reliability. This study proposes a combined Reliability Centred Maintenance (RCM) and prioritisation maintenance strategy with the objective of improving the availability of CMDs. Reliability functions were used in this study as CMDs exhibit Weibull failure distributions identical to general products. The Analytical Hierarchy Process (AHP) was chosen as the maintenance prioritisation strategy because of its ability to analyse complex decisions using subjective and objective criteria. An online survey targeted at hospital managers and Biomedical Engineering Technicians (BMET) was developed to determine the priority of Preventive Maintenance (PM) activities for certain CMD and propose a range of optimal PM intervals for a variety of reliabilities. Results from the prioritisation study have shown that the external environmental effects from COVID-19 had significantly increased the priority for PM of ventilators over other CMDs. Ventilator manufacturers recommend conducting PM once over 12 months or every 5000 hours of operation. The proposed study has revealed that the PM of ventilators can be scheduled once every 14 months to obtain reliabilities close to 100%. Similar outcomes were observed for other CMDs as well. The aftermath of this study will ensure better availability of CMDs, as well as will help to alleviate complications with PM scheduling for managers and BMET departments.

Research and application of reliability-based cost optimization method for constant interval preventive maintenance

Preventive maintenance is a means to ensure the component is kept in the desired state. Lack of preventive maintenance will cause unexpected consequences for the component, and too much preventive maintenance will result in unnecessary investment of resources. Based on the reliability data of the component, this paper establishes an analysis model to determine the optimal preventive maintenance interval of the component to make the cost of preventive and corrective maintenance lowest.

Availability Optimization of Multicomponent Products with Economic Dependence under Two-Dimensional Warranty

For multicomponent products, the maintenance of every component separately will increase the downtime and reduce the availability of products during the warranty period. To solve this problem, the economic dependence between the components is considered in this paper. Firstly, a single-component two-dimensional (2D) preventive maintenance (PM) availability model is established, and the simulated annealing algorithm is adopted to calculate the optimal 2D PM interval to achieve the maximal availability of any single component. Then, to ensure that the warranty cost of each component does not exceed the budget, the PM benchmark interval is introduced, and the PM work is optimized following the method of grouping maintenance. Based on this, the 2D preventive grouping maintenance availability model of multicomponent products is established. Finally, an example is given to verify the proposed method, and the results indicate that the proposed method increases the availability of multicomponent products during the 2D warranty period.

Improved Preventive Maintenance Scheduling for a Photovoltaic Plant under Environmental Constraints

The supply of PV power that satisfies the needs of customers is heavily dependent on the reliability of the generating plants. However, irrespective of the robustness of the design of such physical industrial assets, they tend to depreciate with usage and/or age which, in turn, increases the allowance between the design and the operational capabilities. Therefore, to ameliorate the reliability of the system, a combination of selective and preventive maintenance actions were planned by determining the best combination (optimal preventive maintenance intervals, optimal replaced components). In this work, we developed an optimal preventive maintenance strategy with minimal repair using the iterative numerical technique for a PV plant, with and without considering the influence of environmental conditions on the system. An algorithm was developed on MATLAB to determine the optimal number of preventive maintenance actions that yields the maximum availability by selecting the components to be maintained based on the reliability threshold, without considering the environmental impact on the components. The environmental elements’ criticality was introduced, and the reliability reiterated based on the new technique. Finally, by maximizing the availability of the system, an optimal preventive maintenance for a finite horizon was established.

Integration of balancing and preventive maintenance in straight and U-shaped resource-dependent assembly lines: MILP model and memetic algorithm

In enterprises, machines are subject to some unavailable periods resulting in production halts. The consideration of preventive maintenance (PM) and resource assignment in straight and U-shaped assembly lines is of paramount importance. Hence, this paper aims to tackle the integration of PM and balancing problems in straight and U-shaped resource-dependent assembly lines via two new mixed-integer linear programming (MILP) models and a memetic algorithm. In the MILP models, the optimal PM intervals of all machines and all PM scenarios are determined according to the maximization of the availability. In the memetic algorithm, a multi-attribute solution representation, including heuristic rule matrix, task separator set and machine assignment permutation, is designed to build a feasible solution. For this multi-attribute solution representation, this algorithm respectively designs different crossover, mutation and local search operators to exploit the space around the solutions. Finally, five comparison experiments are conducted to prove the effectiveness of the MILP models in small-scale instances and the superiority of the improvements and memetic algorithm in all instances.

Optimization of Extended Warranty Cost for Multi-Component Products with Failure Interaction

In practice, the assumption of failure independence between components is seldom valid, especially for those complex systems with complicated failure mechanism. Users can decide whether to purchase extended warranty (EW) at the end of basic warranty, and there are many factors that influence this decision, such as product reliability and EW price. In order to solve the problem of EW pricing for multi-component systems with failure interaction reasonably, considering the failure interaction characteristics between components of the multi-component systems, under the condition of type II failure interaction, this paper constructed a dependent failure rate model and developed a EW cost model of two-component systems. Thus, after optimizing the preventive maintenance (PM) strategy, this paper obtained the optimal PM interval when EW cost is the lowest, which is a win-win strategy to reduce the EW price for manufacturers and users under the premise of ensuring the manufacturer's profit demand. Finally, the validity of the model was verified by a numerical example and sensitivity analysis for important parameters was presented.

Group maintenance optimization of subsea Xmas trees with stochastic dependency

Subsea Xmas trees (XTs) are vital equipment for offshore oil and gas development. Due to a long and continuous operation, components of XTs often become vulnerable subjected to degradation and unexpected failures. Due to the uncertainties of subsea operation and fault tolerance design, current maintenances on heterogeneous components, which are assumed to be independent of each other, perform separately. Only one PM mode (imperfect or perfect) is considered. However, these assumptions impede the application of state-of-the-art research results on the maintenance of this equipment. Therefore, for XTs with stochastic dependency, this study proposes a group maintenance optimization approach that combines maintenance activities to reduce maintenance costs. Reduction factors are introduced to measure the effects of various preventive maintenance (PM) actions, and the optimal component-level PM intervals can be obtained. An improved group strategy can be explored in consideration of stochastic dependency and opportunity maintenance. Utilizing the collaborative particle swarm optimization (CPSO) algorithm, the cost of an optimal group maintenance plan can be minimized while maintaining the availability. The uses and advantages of the proposed group maintenance approach are illustrated by a case study on a Horizon Xmas tree with a 14-component system.

Research on Availability Model of Two-dimensional Warranty Products Based on Incomplete Maintenance

In view of the problem that the availability of two-dimensional warranty products is difficult to meet the actual needs of users during the warranty period, on the basis of incomplete preventive maintenance, this paper uses the accelerated failure time (AFT) model to describe the failure rate form of products, constructs the two-dimensional warranty product availability model and gives an effective solution. On this basis, taking the maximum availability of two-dimensional warranty products as the optimization objective, the optimal preventive maintenance interval and warranty period optimization scheme are calculated. Finally, the validity and accuracy of the model are verified by an example, which provides a scientific basis for manufacturers to formulate two-dimensional product warranty strategy.

Availability Optimization of Two-dimensional Warranty Products Under Imperfect Preventive Maintenance

For complex and durable products such as automobiles and construction machinery, the quality and reliability characteristics of these products become more and more complex. However, in many cases, the technical ability of users is not enough to perfect the daily maintenance of the product. Warranty policy plays an increasingly important role between manufacturers and users. Aiming at the problem that the availability of the two-dimensional (2D) warranty product is difficult to meet the actual needs of users during the warranty period, this paper makes use of the accelerated failure time (AFT) model to construct the failure rate function of the product based on imperfect preventive maintenance (PM), establishes the 2D warranty availability model and gives an effective solution. On this basis, this paper obtains the optimal PM interval with the numerical algorithm and particle swarm optimization (PSO) taking the maximal availability of the 2D warranty product as the optimization objective and compares the results and speed of the two algorithms. Finally, the validity and accuracy of the model are verified by a numerical example which provides a scientific basis for the manufacturer to formulate the 2D warranty strategy.

Practical Queueing Models for Preventive Maintenance Plan Optimization: Multiple Maintenance Types and Numerical Studies

Preventive Maintenance (PM) activities in semiconductor manufacturing are important for equipment availability and reliability. Since PMs are down events that remove a tool from service, when to conduct a PM and how to group multiple PM tasks into shared PM events are key issues. In this article, multiple types of PMs with different intervals are considered in G/G/m failure-prone queueing systems. PMs are classified into two categories: time-based preemptive and time-based non-preemptive. For each category of PM, this article provides PM planning optimization models to seek optimal PM intervals that minimize the mean cycle time of wafer lots. Simulation studies are conducted to explore the performance of the optimization model and the mean cycle time approximations. Moreover, sensitivity analysis is conducted and reveals several properties of the PM planning model. Implementation results are briefly reported for a toolset in semiconductor manufacturing that achieved a 12% reduction in mean cycle time using PM plans based on these modelling approaches.

Preventive Maintenance Interval Optimization for Continuous Multistate Systems

Preventive maintenance (PM), which is performed periodically on the system to lessen its failing probability, can effectively decrease the loss caused by the system breakdown or the performance degradation. The optimal PM interval has been well studied for both binary-state systems (BSSs) and discrete multistate systems (MSSs). However, in reality, the performance of many systems can change continuously, ranging from complete failure to perfect functioning. Considering such characteristics of systems, two types of performance-based measures, performance availability and probabilistic resilience, are addressed to quantify the system’s behaviour for continuous MSS. A Monte Carlo-based method is given to analyse the performance change process of the system, and an optimization framework is proposed to find the optimal PM interval with the considerations of per-unit-time cost, system breakdown rate, performance availability, and probabilistic resilience. A computer cluster is used as an example to illustrate the effectiveness of our proposed method.

Maintenance cycle optimisation of multi-component systems under the constraints of overall cost and reliability

Periodic maintenance inevitably leads to 'over-maintenance' or 'non-timely maintenance' of equipment or systems. To improve reliability and reduce maintenance cost, this paper presents a dynamic maintenance cycle optimisation model for multi-component systems. In this paper, the influence of different maintenance intervals on system reliability and total maintenance cost is determined under the conditions that the system has a limited service time and the components are not fully maintained. The model uses the optimisation of system maintenance cost as the objective function to determine the optimal preventive maintenance times and maintenance intervals under the constraint of the lowest reliability of the system. The simulation results of the final example verify that the proposed method can significantly improve the maintenance cost, system reliability, and reliability fluctuation stability. And it can be used to guide subway maintenance cycle optimisation.