Preventive Maintenance Policy Research Articles

Replacement Policy for Heterogeneous Items Subject to Gamma Degradation Processes

A hybrid preventive maintenance policy for heterogeneous degrading items is discussed. It combines the classical age-replacement strategy, when a system is replaced either on failure or at the predetermined age, with replacement of the system when degradation reaches the predetermined level at some intermediate time. Items come from two subpopulations with different reliability characteristics. Non-homogeneous gamma processes model degradation of an item from each subpopulation. We justify probabilistically the superiority of the proposed policy over that for homogeneous populations and over the policy without possibility of additional replacement. The corresponding long-run cost rate is derived for the suggested cost structure. Some detailed numerical illustrations are presented and relevant sensitivity analysis for the main parameters of the model is performed.

Joint optimization of preventive maintenance and inventory management for standby systems with hybrid-deteriorating spare parts

As an effective way to enhance the system reliability, the standby redundancy technique has been widely applied in many industrial systems. Finding methods to determine the maintenance policy and spare-part inventory management is an interesting practical issue. However, due to the imperfect storage and inner mechanism, the spare parts usually deteriorate over time, which not only degrades their performance but also may lead to storage failure. In addition, the immediate burst failure caused by the external shock should not be neglected in storage. These two aspects make the joint optimization of preventive maintenance and inventory policies for a standby system more challenging. Thus, in this study, we first propose a general iterative approach for the lifetime estimation of standby systems with a hybrid spare-part degradation process, which includes both stochastic degradation and immediate burst failure. We then take an example of the Wiener-process-based model to explain how to obtain the analytical results of the lifetime estimation. On this basis, we further establish a joint optimization model, in which the preventive block replacement interval and the inventory number are treated as two decision variables to minimize the expected cost per unit time. Finally, a numerical example and practical case are provided for illustration.

Optimal preventive maintenance policy of the balanced system under the semi-Markov model

Reliability problems of new systems such as balanced systems have received more attention in recent years due to the rapid development of technology. However, the research on maintenance optimization for balanced systems is still underexplored, and the existing policies for orthodox systems cannot solve the maintenance problem of balanced systems. Thus, an optimal preventive maintenance optimization model is formulated under the semi-Markov model for a balanced system with n identical units, where each unit is subject to degradation failure and sojourn times in different function zones follow Erlang distribution with different parameters, respectively. Different from orthodox systems, the failure criterion of the balanced system is given according to the system balance degree. Namely, when any two symmetric components do not perform the same function, the system is out of balance. To avoid system unbalance, a preventive maintenance action is performed once the state difference on any two symmetric units exceeds the maintenance threshold. Finally, some numerical examples are used to demonstrate the priority of the proposed preventive maintenance policy, and the obtained results provide a good insight for repairmen to maintain components on the balanced system.

Comparison of three preventive maintenance warranty policies for products deteriorating with age and a time-varying covariate

The development of condition monitoring technology initiates a new tendency towards incorporating condition-based maintenance (CBM) into warranty policies to reduce the manufacturer's expense over the warranty period. However, CBM is not always more cost-effective than time-based maintenance (TBM), or vice versa. This paper investigates three preventive maintenance (PM) policies for a product sold with a free repair warranty and examines under which situation each policy is optimal. The product deteriorates with both age and a time-varying covariate and its failure rate is described by Cox's proportional hazards model. The covariate can only be revealed by condition monitoring. In policy 1, no inspection is performed and PM actions are carried out periodically. Continuous inspection is performed in policy 2 where a PM action is recommended once continuing operation is not cost-effective. Policy 3 applies periodic inspection to select appropriate maintenance action at each inspection epoch. The warranty servicing costs are analyzed in the Markov decision process framework based on a discretization method. The policy structures of the CBM policies are proved to be of control-limit forms. A practical numerical example is provided to illustrate the proposed approach. The results can help the manufacturer design effective warranty policies.

Preventive replacement policies with multiple missions and maintenance triggering approaches

For the mission-oriented systems, it is not feasible to do any preventive maintenance when the mission is executing. However, the replacement time obtained by traditional replacement policies might be planned during the mission duration. The planned number of missions derived by traditional methods may overestimate the actual value. It would fail to complete the missions or increase the risk of system failure during the mission durations. And the major accident and unimaginable economic losses might occur. Therefore, an appropriate preventive maintenance policy with mission durations should be proposed to guarantee the required reliability and maintainability of the systems. To maintain the large and complex systems, we firstly extend the periodic replacement with minimal repair for systems which are designed to perform multiple missions. Then, two maintenance triggering approaches, i.e., the replacement first and replacement last are discussed, respectively. The optimum replacement time and number of missions are presented analytically and numerically. Finally, the comparison of above replacement policies in maintaining the reconnaissance system of unmanned aerial vehicle is given based on assumed data. The suggested models in this paper can provide better guidance The suggested models in this paper can provide better guidance for maintaining the systems which should implement the periodic replacement policies with minimal repair.

Quasi-Optimal Sizing of a Vehicle Fleet Considering Environmental Impact, Maintenance, and Eventual Containment Measures

In this paper, N types of vehicles having different environmental impacts and different failure rates are considered to perform a set of missions during a predefined period. The sizing problem of the fleet of vehicles is typically based on the literature for the environmental impact of each type of vehicle. This work intends to develop a model that allows considering not only the extent of recourse to non-polluting vehicles but also the preventive maintenance (PM) policy to be adopted for each of the N types of vehicles. More specifically, the objective of this work consists in determining simultaneously the quasi-optimal number of vehicles of each type to be used, the duration of their use, and their average usage rate as well as the period according to which each type of vehicle should be submitted to preventive maintenance. A mathematical model is developed to express and optimize the expected total cost, which includes the costs related to acquisition, operating, maintenance, and environmental impact in addition to considering the resale value. Then, the situation of using the acquired vehicle fleet in a context of a health crisis with containment measures is considered. The latter make it impossible to perform preventive maintenance actions during the containment period. For such situations, given the accumulated degradation in absence of preventive maintenance, the cost model is modified to generate a new preventive maintenance plan to be applied for each vehicle after the containment exit. Numerical results related to fuel and electric vehicles of two brands (Renault and Nissan) are presented and discussed.

Optimal condition-based preventive maintenance policy for balanced systems

Reliability evaluation of balanced systems has been extensively studied in recent years. However, the research on maintenance optimization for balanced systems is still underexplored. This paper makes a novel contribution to existing literature by proposing a condition-based preventive maintenance (PM) policy for balanced systems with identical components. Each component is subject to continuous degradation and the states of all components are simultaneously detected at discrete time epochs. The system state is evaluated based on the deterioration levels of all components. The system is out of balance once the deterioration level of any component reaches a critical value or the difference between the deterioration levels of two components on the symmetric positions exceeds a specific threshold, whichever occurs first. In this research, PM activities are employed to avoid competing failures. The optimal PM thresholds are determined by minimizing the system maintenance cost within the framework of semi-Markov decision process. An illustrative example is provided to demonstrate the application of the proposed PM policy for balanced systems.

Integrating Modelling of Maintenance Policies within a Stochastic Hybrid Automaton Framework of Dynamic Reliability

The dependability assessment is a crucial activity for determining the availability, safety and maintainability of a system and establishing the best mitigation measures to prevent serious flaws and process interruptions. One of the most promising methodologies for the analysis of complex systems is Dynamic Reliability (also known as DPRA) with models that define explicitly the interactions between components and variables. Among the mathematical techniques of DPRA, Stochastic Hybrid Automaton (SHA) has been used to model systems characterized by continuous and discrete variables. Recently, a DPRA-oriented SHA modelling formalism, known as Stochastic Hybrid Fault Tree Automaton (SHyFTA), has been formalized together with a software library (SHyFTOO) that simplifies the resolution of complex models. At the state of the art, SHyFTOO allows analyzing the dependability of multistate repairable systems characterized by a reactive maintenance policy. Exploiting the flexibility of SHyFTA, this paper aims to extend the tools’ functionalities to other well-known maintenance policies. To achieve this goal, the main features of the preventive, risk-based and condition-based maintenance policies will be analyzed and used to design a software model to integrate into the SHyFTOO. Finally, a case study to test and compare the results of the different maintenance policies will be illustrated.

Integrated economic design of quality control and maintenance management: Implications for managing manufacturing process

This paper develops an integrated economic model for the joint optimization of quality control parameters and a preventive maintenance policy using the cumulative sum (CUSUM) control chart and variable sampling interval fixed time sampling policy. To determine the in-control and out-of-control cost for both mean and variance, a Taguchi quadratic loss function and modified linear loss function are used, respectively. Imperfect preventive maintenance and minimal corrective maintenance policies were considered in developing the model, which determines the optimal values for significant process parameters to minimize the total expected cost per unit of time. A numerical example is used to test the model, which is followed by a sensitivity analysis. The integration of CUSUM mean and variance charts with the maintenance actions are proven successful to detect the slightest shift of the process. The findings reveal that among all the cost components, process failure due to external causes and equipment breakdown has a noteworthy attribute to the total costs of the optimized model. It is expected that top managers can utilize the suggested combined model to minimize the costs related to quality loss and maintenance policy and achieve economical advantages as well.

An optimal preventive maintenance policy for a two-stage competing-risk system with hidden failures

In many complex industrial systems, failures are not always evident to the operating crew, thus inspections are used to identify such hidden failures and then make necessary maintenance actions. In this paper, we study a two-stage system that is subject to two competing risks of degradation and random shocks. Failures of this two-stage competing-risk system are hidden and could only be detected by periodic inspections. For such a system, a delay-time model is introduced to describe the evolution of the system behavior. Random shocks are assumed to follow a homogeneous Poisson process. A preventive maintenance policy is presented in response to the observed system state, based on which the expected cost rate of the system is derived. This paper aims to find the minimal expected cost rate by determining the optimal inspection interval. A numerical example is presented to demonstrate the applicability of the proposed method and results.

Joint production and preventive maintenance controls for unreliable and imperfect manufacturing systems

Joint production system control is a challenge for researchers and a daily defy for managers and practitioners. The large concern comes from the interdependence between the system states and the control actions. Several analytical models have addressed these issues but remain inefficient because they are based on many simplifying assumptions for mathematical tractability (mainly concerning the system degradation mode). This is critical because degradation modeling impacts the overall manufacturing system and leads to an over or an under estimation of its performance. In this paper, we propose an efficient stochastic analytical model of integrated production and preventive maintenance control for manufacturing systems subject to operation-dependent degradations of both reliability and quality. A make-to-stock production strategy and an age-based preventive maintenance policy are employed to cope with uncertainty. The main objective is to jointly optimize the production and maintenance control settings by minimizing the total incurred cost. A simulation model is also developed to validate the mathematical model. Numerical examples and a detailed sensitivity analysis are provided to assess the quality of our model and to derive relevant insight and issues regarding the interaction between production, maintenance, and quality.

Optimal periodic preventive maintenance policies for systems subject to shocks

External shock process, known as the shot-noise process, will lead to the failure rate increasing of systems operating under random environment. In this paper, assuming that each shock may cause a random increment of the system failure rate, the optimal periodic preventive maintenance (PM) policy for the system is investigated. The system is preventively maintained at periodic time points nT,n=1,2,…,N−1, and is preventively replaced at time point NT. The PM is not perfect, however, it can remove the accumulated failure rate increments caused by external shocks occurred during the last PM interval. When the system fails during the PM intervals, a minimal repair is performed to restore the function of the system. The expression of the average cost rate of the system is derived explicitly. The existence and uniqueness of the optimal bivariate PM policy (N,T) that minimizes the average cost rate are proved theoretically. Numerical examples are presented to illustrate the maintenance model and show the effectiveness of the proposed method.

Extended block replacement policies with mission durations and maintenance triggering approaches

When the missions arrive, it is not feasible to shut down engineering systems for preventive maintenance policies. Otherwise, it would cause major economic losses and even unimaginable accident. Hence, it is interesting and significative to provide appropriate preventive maintenance policies with mission durations to improve the system reliability and maintainability. For the electronic systems consisting of a block or group of units whose ages are not easy observed and only failures are known, we firstly extend the block replacement policy with random arrival time of mission durations. As a required reliability is needed at the mission time and no replacement can be done during the mission durations preventively. From the points of cost and maintainability, two maintenance triggering approaches, i.e., replacement first and replacement last, are discussed in analytical ways, respectively. Furthermore, three replacement policies are compared theoretically and numerically with optimum mission durations and planned replacement time. In addition, a case illustration in maintaining the electronic systems of active phased array radar (APAR) is given when the arrival time of mission durations follows an exponential distribution and the failure time of electronic systems has a gamma distribution.

A preventive maintenance policy with usage-dependent failure rate thresholds under two-dimensional warranties

This article considers Preventive Maintenance (PM) under a two-dimensional (2-D) warranty contract with time and usage limits. From a manufacturer’s point of view, we develop a dynamic maintenance model with a random horizon to include the impact of random and dynamic usage rates on PM decisions. The model treats the cumulative amount of usage as a state variable that provides information about the failure rate and the expiration of the 2-D warranty. We characterize the optimal PM policy by a sequence of usage-dependent failure rate thresholds. Each threshold is a function of the cumulative usage. Our failure rate threshold policy chooses one of the following two actions in each period: performing perfect PM or no PM. Specifically, the manufacturer should bring the failure rate back to its original level when it exceeds the threshold in the corresponding period. This policy is also optimal under a constant usage rate. In the numerical experiments, we demonstrate the effectiveness of the proposed policy and conduct a sensitivity analysis to investigate how this policy is affected by the model parameters.

Condition-based inspection policies for boiler heat exchangers

This study considers the inspection and maintenance of boiler heat exchangers, which are examples of a system composed of degrading, non-repairable components in series whose operation can be restored by removing failed components from service (albeit at a performance loss). For such systems, the increased failure risk due to component degradation may be managed through inspections and preventive removal of high failure risk components from service (again at a performance loss for remaining life of systems). In this study, a new joint inspection and preventive maintenance policy is developed, which uses condition information obtained at an inspection to optimize decisions regarding maintenance and future inspections to optimally balance the risks of system failure, future performance losses, and maintenance costs. The policy is optimized using the Markov Decision Process paradigm and is applied to a case study of a boiler heat exchanger operating in an Australian sugar factory. The results show that the proposed policy yields significant savings compared to benchmark policies that represent the factory's current practice.

Mathematical model of condition-based preventive maintenance of a complex technical system

A new approach to mathematical modeling of complex technical systems according to their condition is being developed. Connections between subsystems of a complex system are considered to be arbitrary in terms of reliability. Due to wear, failures of subsystems can happen at random moments of time. Failures of some subsystems can lead to the entire system failure. The purpose of the simulation is to maintain the level of reliability and operability of a complex technical system at an optimal level for an unlimited time interval by means of regular preventive maintenance and repair. Technical instructions and specifications, as well as statistical data, are used in modeling a priori characteristics of subsystems. That information is used to determine the reliability of a complex system and its condition. The mathematical model is built in terms of the Markov decision-making process. The chosen optimization method allows obtaining the best policy for choosing acceptable preventive maintenance policy and repairs at the planned time of inspections and moments of failures.

Optimal preventive 'maintenance-first or -last' policies with generalized imperfect maintenance models

<p style='text-indent:20px;'>This paper presents modified preventive maintenance policies for an operating system that works at random processing times and is imperfectly maintained. The system may suffer from one of the two types of failures based on a time-dependent imperfect maintenance mechanism: type-Ⅰ (minor) failure, which can be rectified by minimal repair, and type-Ⅱ (catastrophic) failure, which can be removed by corrective maintenance. When the system needs to be maintained, two policies "preventive maintenance-first (PMF) and preventive maintenance-last (PML)" may be applied. In each maintenance interval, before any type-Ⅱ failure occurs, the system is maintained at a planned time <inline-formula><tex-math id="M1">\begin{document}$ T $\end{document}</tex-math></inline-formula> or at the completion of a working time, whichever occurs first and last, which are called PMF and PML, respectively. After any maintenance activity, the system improves but its failure characteristic is also altered. At the <inline-formula><tex-math id="M2">\begin{document}$ N $\end{document}</tex-math></inline-formula>-th maintenance, the system is replaced rather than maintained. For each policy, the optimal preventive maintenance schedule (<inline-formula><tex-math id="M3">\begin{document}$ T $\end{document}</tex-math></inline-formula>, <inline-formula><tex-math id="M4">\begin{document}$ N $\end{document}</tex-math></inline-formula>)<inline-formula><tex-math id="M5">\begin{document}$ ^{*} $\end{document}</tex-math></inline-formula> that minimizes the mean cost rate function is derived analytically and determined numerically in terms of its existence and uniqueness. The proposed models provide a general framework for analyzing the maintenance policies in reliability theory.

Optimal preventive maintenance policy for a system subject to two-phase imperfect inspections

Inspections are essential to finding the health status of a system. However, inspections are usually imperfect, which may not always reveal the true state of the system. Most of research on imperfect inspections of degrading systems focuses on one-phase inspections, but a multi-phase inspection strategy may be more flexible and efficient. In this paper, a two-phase imperfect inspection strategy is considered along with a hybrid preventive maintenance policy for a three-state system. To minimize the maintenance cost, the optimal inspection interval and number of inspections in each phase are obtained based on the renewal reward theorem. Numerical examples are provided to illustrate the proposed method and to explore some managerial insights. To show the advantages of the proposed method, several special models, such as perfect inspection model, one-phase inspection model, and pure inspection model, are presented for comparison. One can see that taking account of imperfect inspections in maintenance management is important, the two-phase inspection policy is generally better than the one-phase inspection, and the pure inspection model might be cost-optimal for the system with a constant defect rate.

A Generalized Chart-Based Decision-Making Tool for Optimal Preventive Maintenance Time under Perfect Renewal Process Modeling

The most commonly used probabilistic model in reliability studies is the Perfect Renewal Process (PRP), which is characterized by the condition or type of maintenance represented: once the maintenance activities are executed, the equipment is restored to its original condition, leaving it “as good as new.” It is widely used since it represents an optimistic state when an item is replaced, assuming a perfect operational condition of the item after the maintenance. Some models have been developed for determining optimum preventive maintenance (PM) based on different criteria, and almost all aimed at PRP reliability modeling. The contribution of this paper is to analyze a model for determining the optimal preventive maintenance policy for a long time run under PRP and developing a general and chart-based tool for the problem, making it easier to solve the day-to-day practice and operation of equipment. As a result, a generalized chart was developed to support maintenance decisions through the elaboration of an original isometric table and complemented with a step-by-step methodology to determine the optimum time in which the preventive maintenance activities must be implemented. In most cases, these types of maintenance activities will consider a replacement activity.

Availability analysis of an offshore oil and gas production system subjected to age-based preventive maintenance by Petri Nets

The main objective of this paper is to model an offshore oil production system subjected to age-based preventive maintenance strategies by Petri Nets and to evaluate its availability by Monte Carlo Simulation. The oil processing and the separation equipment with their reliability and maintainability characteristics, the corrective and preventive maintenance policies and the operational dependencies that lead to the reconfiguration of the system after the failure are implemented. A special attention is given to the effect of age-based perfect and imperfect preventive maintenance strategies on the system availability. The maintenance actions consider the components’ age thresholds and an age reduction ratio. Moreover, the variation of the oil and gas flows from the well over the years is accounted by the model. As case study, an offshore production installation that operates in a Brazilian oilfield is adopted. An elasticity analysis on the model parameters is conducted to assess influence of the maintenance policy on the system availability and on the oil production.