Periodic Maintenance Policy Research Articles

Data-driven preventive maintenance for a heterogeneous machine portfolio

We describe a data-driven approach to optimize periodic maintenance policies for a heterogeneous portfolio with different machine profiles. When insufficient data are available per profile to assess failure intensities and costs accurately, we pool the data of all machine profiles and evaluate the effect of (observable) machine characteristics by calibrating appropriate statistical models. This reduces maintenance costs compared to a stratified approach that splits the data into subsets per profile and a uniform approach that treats all profiles the same.

Optimum periodic maintenance policy of repairable multi-component system with component reallocation and system overhaul

Motived by cost savings, this paper studies a new periodic maintenance policy for multi-component systems, for which multiple component reallocations and system overhauls are jointly implemented aperiodically at fixed times and components are minimally repaired immediately after failures in a life cycle. Different workloads or conditions of different positions can lead to different degradation processes of components, thus component reallocation can be used to balance the degradation between components at different positions, which has been widely studied. System overhaul can improve the conditions of components by imperfect or perfect repair. Component reallocation and system overhaul are mutually dependent and jointly affect the degradation of components and the maintenance cost of a system. In this paper, the failure rate functions of components are modeled using virtual age, and then a binary mixed integer nonlinear programming model is established to determine the optimal time and assignment for component reallocations, and the optimal time and degree for system overhauls by minimizing the long-run average maintenance cost of the system. Further, the number of system overhauls is proved to be finite, and a decomposition method is proposed. Numerical examples are provided to show the effectiveness of the joint of component reallocations and system overhauls.

Condition-Based Maintenance with Reinforcement Learning for Dry Gas Pipeline Subject to Internal Corrosion.

Gas pipeline systems are one of the largest energy infrastructures in the world and are known to be very efficient and reliable. However, this does not mean they are prone to no risk. Corrosion is a significant problem in gas pipelines that imposes large risks such as ruptures and leakage to the environment and the pipeline system. Therefore, various maintenance actions are performed routinely to ensure the integrity of the pipelines. The costs of the corrosion-related maintenance actions are a significant portion of the pipeline’s operation and maintenance costs, and minimizing this large cost is a highly compelling subject that has been addressed by many studies. In this paper, we investigate the benefits of applying reinforcement learning (RL) techniques to the corrosion-related maintenance management of dry gas pipelines. We first address the rising need for a simulated testbed by proposing a test bench that models corrosion degradation while interacting with the maintenance decision-maker within the RL environment. Second, we propose a condition-based maintenance management approach that leverages a data-driven RL decision-making methodology. An RL maintenance scheduler is applied to the proposed test bench, and the results show that applying the proposed condition-based maintenance management technique can reduce up to 58% of the maintenance costs compared to a periodic maintenance policy while securing pipeline reliability.

Application of Risk-Based Maintenance Using Analytic Hierarchy Process for Selection of Maintenance Policy of Dragline

This paper proposes an approach to select the optimum maintenance policy for mining equipment using risk-based maintenance (RBM). RBM constitutes the process of risk assessment that identifies high-risk components of equipment, and analytic hierarchy process (AHP) that helps selecting an optimum maintenance policy for individual components. By applying RBM, three high-risk components of dragline such as reduction gearbox, bearing, and motor pinion shaft have been identified,and then AHP was used on these three components by considering four criteria, eight sub-criteria and four alternative maintenance policies (such as periodic, preventive, predictive and corrective maintenance). Local and global scores for these three components were calculated from the opinion of three experts of the case study mine. AHP global scores revealed that the optimum maintenance policy for reduction gearbox and motor pinion shaft was preventive maintenance, whereas for bearing it was periodic maintenance policy. Adoption of such maintenance planning is expected to reduce maintenance cost and downtime of the dragline.

Optimal periodic maintenance policies for a parallel redundant system with component dependencies

In this paper, we investigate modified periodic maintenance policies for a parallel redundant system with three types of dependencies including stochastic dependence, structural dependence, and economic dependence, where they are seldom taken into account simultaneously in previous researches. More specifically, the stochastic dependence is modelled when multiple external shocks selectively affect the distinct components according to their sizes, attributes, and functions, leading to the existence of common cause failure. In addition, the structural dependence is considered as the parallel system is packaged and just systematic preventive maintenance is available. Finally, the economic dependence is introduced when developing preventive maintenance models, where the replacement costs among all items are associated. System reliability function is derived analytically in terms of cumulative shock damage theory and Laplace transform, and then four modified periodic maintenance policies are developed, in which the expected maintenance cost rate is minimized to seek the optimal replacement interval. An illustrative example of a silicon micro-electro-mechanical system (MEMS), which is composed of multiple non-identical resonators in parallel, is arranged to validate the theoretical results and to show the significance of considering component dependencies when scheduling preventive maintenance policies.

Design of multi-component periodic maintenance programs with single-component models

ABSTRACTCapital assets, such as wind turbines and ships, require maintenance throughout their long lifetimes. Assets usually need to go offline to perform maintenance, and such downs can be either scheduled or unscheduled. Since different components in an asset have different maintenance policies, it is key to have a maintenance program in place that coordinates the maintenance policies of all components, to minimize costs associated with maintenance and downtime. Single-component maintenance policies have been developed for decades, but such policies do not usually allow coordination between different components within an asset. We study a periodic maintenance policy and a condition-based maintenance policy in which the scheduled downs can be coordinated between components. In both policies, we assume that at unscheduled downs, a minimal repair is performed to keep the unscheduled downtime as short as possible. Both policies can be evaluated exactly using renewal theory, and we show how these policies can be used as building blocks to design and optimize maintenance programs for multi-component assets.

A condition-based maintenance model for a single component in a system with scheduled and unscheduled downs

We introduce a maintenance model for a single component that is part of a complex engineering system and has a monotic, stochastic degradation process. For this component, a warning limit is given and we use a condition-based maintenance (CBM) policy. The other components in the system are assumed to follow failure-based and periodic maintenance policies and their maintenance actions lead to unscheduled and scheduled downs of the system. These downs constitute opportunities for the single CBM component. If the degradation is at or above a control limit at an unscheduled or scheduled down, the component is replaced preventively and one saves on downtime and setup costs. We derive an efficient and accurate approximate evaluation procedure. Further, we show that this approximate evaluation leads to a close-to-optimal control limit policy when used within the optimization procedure and we show the potential savings when both unscheduled and scheduled downs are used as opportunities for the CBM component. Finally, we demonstrate that our model can be well used as a building block to solve multi-component CBM problems with many components.

Optimal periodic maintenance policy under imperfect repair: A case study on the engines of off-road vehicles

ABSTRACTIn the repairable systems literature one can find a great number of papers that propose maintenance policies under the assumption of minimal repair after each failure (such a repair leaves the system in the same condition as it was just before the failure—as bad as old). This article derives a statistical procedure to estimate the optimal Preventive Maintenance (PM) periodic policy, under the following two assumptions: (i) perfect repair at each PM action (i.e., the system returns to the as-good-as-new state) and (ii) imperfect system repair after each failure (the system returns to an intermediate state between as bad as old and as good as new). Models for imperfect repair have already been presented in the literature. However, an inference procedure for the quantities of interest has not yet been fully studied. In the present article, statistical methods, including the likelihood function, Monte Carlo simulation, and bootstrap resampling methods, are used in order to (i) estimate the degree of efficiency of a repair and (ii) obtain the optimal PM check points that minimize the expected total cost. This study was motivated by a real situation involving the maintenance of engines in off-road vehicles.

Optimization of production and maintenance problem: Case study of wind turbine power generation

W turbine system reliability is a critical factor in the success of a wind energy project. Consequently, maintenance is a crucial issue for such projects. In this paper, an integrated production and periodic maintenance policy with minimal repair at failures is proposed for a hybrid power generation system. We seek to establish an optimal integrated electricity production and maintenance plan over a finite planning horizon. It is important to do so according to the wind speed profile in order to guarantee that a random power demand (consumption) is satisfied given a target service level. In the context of energy planning, many researchers presented several techniques and methods which provide solutions to the multi-objective optimization problem for energy planning decisions. They have observed that Analytical Hierarchy Process (AHP) is the most popular technique followed by outranking techniques Promethee and Electre. Regarding renewable power generation systems reliability, some researchers studied the factors that contribute to the reliability deterioration of such systems; they concluded that in a significant part of the year, seasonal and spatial variability of wind, and to a lesser extent that of solar energy can yield low availability leading to an increase in unserved energy. Based on these literature reviews, we propose in this talk an integrated production-maintenance policy for wind turbine power generation. In fact, our approach consists in determining the best combination of energy production and maintenance effort, which minimizes the total cost while satisfying a random demand during a finite horizon with a given service level. This study is inspired from the background work, which models the influence of the production rate on the degradation level of the production system, and consequently on the maintenance strategy. A numerical example illustrates the use of the developed mathematical model.

A MULTI-OBJECTIVE APPROACH TO OPTIMIZE A PERIODIC MAINTENANCE POLICY

The present paper proposes a multi-objective approach to find out an optimal periodic maintenance policy for a repairable and stochastically deteriorating multi-component system over a finite time horizon. The tackled problem concerns the determination of the system elements to replace at each scheduled and periodical system inspection by ensuring the simultaneous minimization of both the expected total maintenance cost and the expected global system unavailability time. It is assumed that in the case of system elements failure they are instantaneously detected and repaired by means of minimal repair actions in order to rapidly restore the system. A nonlinear integer mathematical programming model is developed to solve the treated multi-objective problem, whereas the Pareto optimal frontier is described by the Lexicographic Goal Programming and the ε-constraint methods. To explain the whole procedure, a case study is solved and the related considerations are given.

On determination of the preventive maintenance interval guaranteeing system availability under a periodic maintenance policy

In order for an engineering system to maintain a certain level of availability, preventive maintenance actions are commonly performed. This paper deals with the analysis for determining the optimum frequency to perform preventive maintenance actions for a system exhibiting a linearly increasing hazard rate and a constant repair rate. Under a periodic maintenance policy, as long as a predetermined availability is guaranteed, the time interval between preventive maintenance actions incurring the minimal maintenance cost is selected. Considering both the maintenance cost and system availability, guidelines for determining the maintenance time interval are presented. Simple examples are also provided to illustrate these ideas.

A modified imperfect preventive maintenance policy considering failure replacement

This article is an extension of the periodic imperfect maintenance model in the literature. The purpose of this research is to investigate the influence on the optimal periodic maintenance policy after considering the failure replacement in the last period of preventive maintenance (PM). The specific maintenance decisions for a repairable system are: when to replace the system, when to repair the system; whether to conduct PM or keep the system operating. A partially periodic PM policy is proposed incorporating the costs of minimal repair, PM, failure replacement, and preventive replacement. The average cost rate for the proposed policy is obtained. Finally, the optimal parameters of the maintenance policy can be calculated using the cost rate function and the numerical comparisons of different policies are provided to demonstrate the effect of failure replacement.

GUIDELINES FOR IMPLEMENTING PREDICTIVE MAINTENANCE*

Recent advances in predictive maintenance technologies have led many manufacturers to abandon traditional periodic maintenance policies and replace them with predictive maintenance policies. The models in this paper explicitly evaluate the decision to utilize both predictive and periodic maintenance when the objective is to minimize expected maintenance costs per unit time. Renewal theory is used to obtain optimal policies as unique solutions of integral equations that depend on the failure distribution and prediction capabilities. Based on this research, we recommend that practitioners do not abandon the traditional maintenance methods but follow our guidelines for utilizing periodic maintenance in conjunction with the new technologies.

Cost minimization for periodic maintenance policy of a system subject to slow degradation

Abstract This paper considers a repairable system which undergoes preventive maintenance (PM) periodically and is minimally repaired at each failure. Most preventive maintenance models assume that the system improves at each PM so that the hazard rate is reduced to that of a new system or to some specified level. In this paper, we consider the situation where each PM relieves stress temporarily and hence slows the rate of system degradation, while the hazard rate of the system remains monotonically increasing. The optimal number and period for the periodic PM that minimize the expected cost rate per unit time over an infinite time span are obtained. We also consider the case when the minimal repair cost varies with time. Explicit solutions for the optimal periodic PM are given for the Weibull distribution case.

Optimal periodic maintenance policy for machines subject to deterioration and random breakdown: D. N. Khandelwal, Jaydev Sharma and L. M. Ray. IEEE Trans. Reliab., R-28, (4) 328 (October 1979)

We derive three equivalent sufficient conditions for association in time of a finite state semi-Markov process in terms of transition probabilities and crude hazard rates. This result generalizes the earlier results of Esary and Proschan (1970), for a binary Markov process and Hjort et al. (1985), for a multistate Markov process.