Age-based Preventive Maintenance Research Articles

On preventive maintenance of k-out-of-n systems subject to fatal shocks

In this paper, we investigate optimal age-based preventive maintenance (PM) policies for an ( n- k + 1)-out-of- n system whose components are exposed to fatal shocks that arrive from various sources. We consider two different scenarios for the system failure. In the first one, it is assumed that the shock process is of the type of Marshall-Olkin where each shock affects one component of the system and puts it down, and one shock affects all components and destroys all of them. In the second scenario, it is assumed that the system is subject to an extended type of Marshall-Olkin shock process where the shocks arriving at random times may cause the breakdown of 1, 2, …, or n components. Under each scenario for the components failure, we investigate an optimal age-based PM model for the system by imposing the related cost function. Then, in each case, we explore the optimal PM time that minimizes the mean cost per unit of time. Some numerical results are presented to illustrate the applications of the proposed models.

Joint optimization of inspection-based and age-based preventive maintenance and spare ordering policies for single-unit systems

This paper presents a joint optimization policy of preventive maintenance (PM) and spare ordering for single-unit systems, which deteriorate subject to the delay-time concept with three deterioration stages. PM activities that combine a nonperiodic inspection scheme with age-replacement are implemented. When the system is detected to be in the minor defective stage by an inspection for the first time, place an order and shorten the inspection interval. If the system has deteriorated to a severe defective stage, it is either repaired imperfectly or replaced by a new spare. However, an immediate replacement is required once the system fails, the maximal number of imperfect maintenance (IPM) is satisfied or its age reaches to a pre-specified threshold. In consideration of the spare's availability as needed, there are three types of decisions, i.e., an immediate or a delayed replacement by a regular ordered spare, an immediate replacement by an expedited ordered spare with a relative higher cost. Then, some mutually independent and exclusive renewal events at the end of a renewal cycle are discussed, and the optimization model of such a joint policy is further developed by minimizing the long-run expected cost rate to find the optimal inspection and age-replacement intervals, and the maximum number of IPM. A Monte-Carlo based integration method is also designed to solve the proposed model. Finally, a numerical example is given to illustrate the proposed joint optimization policy and the performance of the Monte-Carlo based integration method.

Availability Analysis of an Offshore Wind Turbine Subjected to Age-Based Preventive Maintenance by Petri Nets

This paper analyses the production availability and the associated maintenance costs of an offshore wind turbine with a horizontal axis configuration using Petri Nets modelling with Monte Carlo Simulation. For this purpose, different features are implemented: the reliability and maintainability characteristics of the components; the logistics of the production and maintenance operations, including different types of vessels, the mobilization time, costs and weather window. The maintenance strategies consist of corrective maintenance and age-based imperfect preventive maintenance that depends on the components’ age and age reduction ratio. Thereby, to increase the operating income and to reduce the costs associated with the operation and maintenance activities, the optimal parameters of the age-based preventive maintenance are estimated. As a case study, a generic offshore wind turbine that operates at the Viana do Castelo wind farm in Portugal is adopted. The wind farm is located 18 km off the shore. The turbine’s total exploration life is 25 years.

Reliability and hybrid maintenance modeling for competing failure systems with multistage periods

This paper deals with the reliability and hybrid maintenance models for systems which are subjected to competing and dependent failures due to degradation and external shocks. System studied here are with multistage periods, standby stage, working stage and the stop stage, which makes it different with previous literatures. The characters of degradation and external shocks vary with different periods and this also makes the proposed reliability model more challenging and more consistent with the engineering reality. Besides, a hybrid maintenance policy combined by age-based preventive maintenance and condition-based maintenance is studied by minimizing the average long-run maintenance cost. The optimal age-based preventive interval and the condition-based threshold are both calculated to manage the minimum maintenance cost. A numerical case demonstrates the availability and general applicability of proposed reliability and maintenance models at the end of this paper.

Integrated production and maintenance control policies for failure-prone manufacturing systems producing perishable products

The problem of integrated production and maintenance control of unreliable manufacturing systems evolving in a stochastic and dynamic environment is studied in this paper. The considered system is subject to degradation and the produced products are perishable with random shelf-lives. The literature of operations management for perishable products reports a correlation between the shelf-life of the product and the machine degradation. In fact, the latter results in shelf-lives reduction. Ignoring this correlation effect may result in inaccurate values of the shelf-lives and inefficient control policies. The objective of this paper is to develop a joint production and maintenance control policy that minimizes the total cost composed of backlog, inventory holding, disposal, and maintenance costs. The proposed parametrized joint control policy combines a multi hedging point policy and an age-based preventive maintenance policy. The optimization of the parameters of the proposed joint control policy is obtained using a simulation-based optimization approach and sensitivity analyses are provided to confirm its robustness. The obtained results show that the correlation between machine degradation and shelf-life reduction has a major influence on the control parameters and that preventive maintenance interventions can lead to increasing the shelf-life of products and minimizing the total cost. The proposed joint control policy is then compared to three other polices for a wide range of system and cost data. The obtained results show that the proposed joint control policy outperforms the other polices in terms of total incurred costs.

An Efficient Jaya Algorithm for Joint Optimization of Preventive Maintenance and Quality Policy in Production Systems

The present paper develops an integrated approach for joint optimization of preventive maintenance (PM) planning and quality control policy in a single machine manufacturing system. Considering the economic dependency between quality and maintenance parameters, the proposed model aims to optimize the four decision variables, viz. PM interval, sample size, sample frequency and control limit coefficient to minimize the expected total integrated cost per unit time. The imperfect age-based PM model is applied which addresses the most practical issue that a unit may not always be maintained to an as-good-as-new state. Analysing the problem to be NP-hard in nature, a recently developed meta-heuristic named Jaya algorithm is applied to retrieve the near-optimum solutions. The peculiarity of Jaya as compared to conventional heuristics is that it is a parameter-less algorithm thus does not require tuning to algorithm-specific parameters thereby minimizing computational complexity to a great extent. A numerical example is presented to demonstrate the effectiveness of the integrated economic design of PM and process quality control parameters. Computational results reveal the effectiveness of the proposed approach.

Age-based preventive maintenance with multiple printing options

In today's economic context, production systems must be readily available and machinery downtime kept to a minimum. Maintenance and spare parts inventory management play a vital role in achieving these goals, and preventive maintenance has increasingly been considered in maintenance policies. Additive manufacturing (AM) has recently been combined with preventive maintenance, and thus represents an emerging research direction. However, few studies have as yet been conducted in this research stream, and we intend to fill this gap. Our study makes three main contributions. First, we address the main limitations of two current models (i.e., assuming that no failure occurs during the replenishment lead time of the spare parts). Second, we propose a new maintenance policy that considers two printing options with different levels of reliability and unitary purchase costs. Third, we develop a decision support system (DSS) to assist managers in deciding whether to implement a preventive maintenance policy that includes AM or conventional manufacturing (CM) parts. We take an interdisciplinary approach to conducting a parametrical analysis where we consider real data on the reliability of CM and AM parts, in addition to the impact of post-processing operations and optimization routines. We find that AM-based preventive maintenance policies are favored when the MTTF and the backorder costs are low and when the failure and maintenance costs are high. These findings have been incorporated into the DSS, which provides thresholds for every parameter to guide practitioners in choosing between AM and CM parts for preventive maintenance, without requiring time-expensive calculations.

Optimal Age-Based Preventive Maintenance Policy for a System Subject to Cumulative Damage Degradation and Random Shocks

This research investigates the problem of cumulative degradation and random shocks a system like a centrifugal pump may experience during normal and adverse operating conditions. An accelerated life testing method was employed to determine the degradation of the pump under cumulative damage degradation and random shocks conditions. An age- Based policy was used to determine the optimum time interval that will minimize the total expected cost of the system. The random shock increases the number of failures and hence reduces the reliability of the system. The total expected preventive maintenance cost obtained varies from N1700.00 (One thousand seven hundred naira) to N16,000.00 (sixteen thousand naira), depending on the shock and shock duration. The methodology presented is useful and thus recommended for use to study cumulative damage degradation and random shocks for similar systems.

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.

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.

Age-based preventive maintenance for coherent systems with applications to consecutive-k-out-of-n and related systems

This article presents a signature-based representation for the expected cost rate of age-based preventive maintenance policy for a binary coherent system consisting of independent exponential components, and then specializes the method to consecutive k-out-of-n system and its generalizations. According to the age-based preventive maintenance policy, the system is replaced at failure or before failure. For an arbitrary coherent system, the number of failed components at replacement time is a random variable. Thus, the expected cost per unit of time involves the mean number of failed components at replacement time. This mean is represented in terms of signature. Extensive numerical and graphical examples are presented for m-consecutive k-out-of-n:F and consecutive-k-within-m-out-of-n:F systems.

Preventive Maintenance with a 3D Printing Option

Additive manufacturing (AM) technology, or 3D printing as it is otherwise known, has been developing rapidly. Among other things, it enables on-demand printing of spare parts via local 3D printing hubs. This theoretically eliminates the need for firms to keep spare parts inventory, even though such printed parts may not be as reliable as regular spare parts. To investigate this trade-off we develop two age-based preventive maintenance policies in which on-demand printed parts are used to protect against long system downtime. In the first policy the printed parts are only used temporarily, i.e., until a regular spare part arrives, while in the second policy the printed parts are subject to their own age-based preventive maintenance threshold. These printing policies are benchmarked against a common age-based preventive maintenance policy for which a regular part is held on stock. We find that the printing policies outperform this benchmark in terms of costs in many cases, but not in all. Specifically, on-demand printing should be used in a preventive maintenance setting when holding costs and direct failure costs are high. We furthermore find only small difference in performance between the two printing policies. These parts should be subjected to a preventive maintenance threshold only when printed parts are expected to last for a significant amount of time (i.e., several times the magnitude of the regular spare part lead time).

Joint Economic Design of CUSUM Control Chart and Age-Based Imperfect Preventive Maintenance Policy

The close relationship between statistical process control and maintenance has attracted lots of researchers to focus on the jointly economic design of control chart (a main tool of statistical process control) and preventive maintenance policy, and much progress has been made in this field. However, in the existing literatures, the X- chart is used most, and other charts are rarely considered. In this paper, the economic design of CUSUM chart and age-based imperfect preventive maintenance policy is presented. The process is considered as a multiphase system, and a recursive algorithm is used to model each phase. Besides, a sampling policy under the non-Markovian deterioration assumption is employed, and an age-based imperfect preventive maintenance policy is used. An optimization model with the objective of minimizing the expected cost per unit time is constructed to obtain the near-optimal solution of decision variables: the age of the machine for maintenance, the number of age-based maintenances, sample size, sampling intervals, and the decision interval coefficient and reference value coefficient of CUSUM chart. The solution procedure of the model is provided. Also, sensitivity analysis is performed on the decision variables for each of the various parameters.

A model of system limiting availability under imperfect maintenance

PurposeThe purpose of this paper is to explore the impact of the Kijima Type II imperfect repair model on the availability of repairable systems (RS). Since many individuals are interested in measuring the extent to which the system will be available after it has been run for a long time, the specific interest in this study is in the steady-state (limiting) availability behavior of such systems. Furthermore, the authors study the impact of age-based preventive maintenance (PM) on the RS performance.Design/methodology/approachBecause of the complexity of the underlying assumptions of the Kijima Type II model, the authors use simulation modeling to estimate the system availability. Based on preliminary simulation results, the availability function achieves a steady-state value greater than zero. The system steady-state availability is then estimated from the simulation output by computing the average of the availability estimates beyond the initial transient period. Next, the authors develop a meta-model to convert the system reliability and maintainability parameters into the coefficients of the limiting availability estimate without the simulation effort. Using a circumscribed central composite experimental design, the authors confirm the accuracy of the meta-model.FindingsThe results show that the meta-model is robust, and provides good estimates of the system limiting availability. Also, the authors find that when using a Kijima Type II model for a system repair process, age-based PM can improve the steady-state availability value. Therefore, an optimal age-based PM policy that maximizes the system’s steady-state availability can be identified.Originality/valueIn practice, it is important to study the system steady-state availability because many individuals, i.e. engineers, are more interested in measuring the extent to which the system will be available after it has been run for a long time. Therefore, this study represents a significant addition to the body of knowledge related to virtual age modeling, in that it incorporates a Kijima type II model and considers system steady-state availability.

Analytical properties of an imperfect repair model and application in preventive maintenance scheduling

The paper considers repairable systems under imperfect repair. The failure rate of a new system is assumed to follow a Weibull distribution and the repair efficiency is characterized by a Kijima type II virtual age model named Arithmetic Reduction of Age with infinite memory. An analytical approach to obtain the distribution of the inter-failure times is presented. The existence of a stationary regime is highlighted and the limiting distributions are explicitly derived. In this context, an optimal age-based preventive maintenance policy can be implemented. Three approaches are proposed, considering a static, a dynamic or a failure limit policy. Numerical simulations are presented to illustrate the policies.

Optimal Long-Run Imperfect Maintenance With Asymptotic Virtual Age

We consider imperfect, periodic preventive maintenance of items with increasing failure rates, and minimal repairs in-between. The imperfect maintenance is performed in accordance with a Kijima II virtual age model. The obtained deterministic asymptotic value of virtual age defines the starting age of an item at the beginning of each cycle in the formulated and analyzed long-run optimization problem. A similar approach is used for the corresponding age-based preventive maintenance, where the starting age of the renewal-type cycle is already a random variable. Some meaningful examples are considered.

Joint planning for spare parts inventory and preventive maintenance in a multi-echelon network

Spare parts inventory and preventative maintenance play a vital role in ensuring operational availability of capital equipment. This paper investigates the cost-reliability benefits from jointly optimising preventive maintenance and spare parts inventory. Due to the problem complexity and the mathematical intractability, we propose a simulation framework to tackle this joint planning problem in a multi-echelon network. The general simulation model developed in this paper allows a closer-to-reality analysis of joint decisions on spare parts inventory and preventive maintenance for a fleet of equipment with increasing failure rates. Both block- and age-based preventive maintenance policies are considered. We also adopt a performance-based contracting approach to analysing the trade-off between inventory cost and target availability in different incentive mechanisms. Numerical experiments are presented to illustrate the effectiveness of the joint policies.

Modelling and simulation of the operation and maintenance of offshore wind turbines

The offshore environment limits the accessibility to the wind turbines and subjects them to faster degradation processes than in onshore. Thus, operation and maintenance is more challenging and costly and represents a considerable share of the cost of energy. It is therefore important to identify which factors most influence the turbines’ performance, namely, the availability, overall cost and revenues, so that actions can be taken to minimize their effect. This article addresses such issues by presenting a parametric study on how the variation of failure and repair models, vessels logistic times, weather windows and waiting times affect a wind turbine performance. Offshore failure models/data were not usually available on the public domain, being obtained herein from onshore ones using an empirical approach based on stress factors for mechanical systems. The baseline model results from the optimization of an operation and maintenance strategy based on corrective maintenance replacements and imperfect age-based preventive maintenance repairs. Generalized stochastic Petri nets with predicates coupled with Monte Carlo simulation are used for modelling and simulation. Results are discussed.

A stochastic alternating renewal process model for unavailability analysis of standby safety equipment

The paper presents a stochastic approach to analyze instantaneous unavailability of standby safety equipment caused by latent failures. The problem of unavailability analysis is formulated as a stochastic alternating renewal process without any restrictions on the form of the probability distribution assigned to time to failure and repair duration. An integral equation for point unavailability is derived and numerically solved for a given maintenance policy. The paper also incorporates an age-based preventive maintenance policy with random repair time. In case of aging equipment, the asymptotic limit or average unavailability should be used with a caution, because it cannot model an increasing trend in unavailability as a result of increasing hazard rate (i.e. aging) of the time to failure distribution.

JOINT MAINTENANCE AND SPARE COMPONENT PROVISIONING POLICY FOR k-OUT-OF-n SYSTEMS

This study develops an analytic framework for joint modeling of age-based preventive maintenance and (s, Q) spare component provisioning policy for k-out-of-n:G systems with i.i.d. nonrepairable components. We assume that during system maintenance, instead of discarding the whole system, only failed components are replaced by spares if the on-handspares are sufficient. The objective is to determine a proper preventive maintenance interval and the ordering parameters to minimize the undiscounted long run average cost under a system availability constraint. Our study sheds light on the lumpy demand scenario in the problems of joint modeling of preventive maintenance and spare provisioning.