Preventive Maintenance Policy Research Articles

Reliability analysis and optimal replacement for a k-out-of-n system with various components subject to shocks

Consider a k-out-of- n system consisting of L types of components, L=1,2,…,n such that the number of components of the l th type is nl, l=1,2,…,L . Let this system is exposed to external shocks, and each entered shock may fail some components of the system. The system fails when at least n-k+1 components fail. In this paper, we study the reliability of this system under a shock model. In addition to reliability analysis, we investigate a preventive maintenance policy for this system to reduce abrupt failures. Numerical examples are also provided to illustrate theoretical achievements.

Reliability analysis and preventive maintenance policy for consecutive k$k$‐out‐of‐n:F$n: F$ balanced system under failure criterion operating in shock environment

AbstractThis paper presents a consecutive ‐out‐of‐: balance system in a shock environment, where the state transition of component is induced by external shocks. If a predetermined threshold number of effective shocks are applied to component in a critical state, the component will fail. The state of the system is defined by the number of consecutive failing component groups in the system, which leads to system failure when a critical number of consecutive failing components is reached. To minimize maintenance costs, we propose a preventive maintenance method with an optimization model. We use finite Markov chain imbedding and Phase‐type distribution to calculate component group failure rates and associated probability functions in discrete and continuous time, respectively. The validity and accuracy of the model are confirmed by numerical examples and Monte Carlo simulations.

A scalable optimization approach to the intervention planning of complex interconnected infrastructures

The functioning of infrastructure networks is vital for modern communities. Maintenance should be planned to ensure infrastructure's functionality and safety at the lowest cost. Interconnected infrastructure networks can affect each other's functionality, and maintenance on one network can impact the serviceability of others. Planned intervention grouping across infrastructures reduces set-up costs and service interruption, improving infrastructure availability and serviceability at lower costs.Finding the best grouping strategy is a known NP-hard problem, with several optimization strategies have been proposed, mainly based on nonlinear models which are computationally expensive and do not guarantee scalability. Furthermore, infrastructure intervention planning models mostly focus on grouping of interventions which are considered as given. In this paper, we propose a new efficient optimization model to optimize intervention grouping for interconnected infrastructure networks. We develop a scalable two-step optimization model where we first plan each individual intervention type based on a preventive maintenance policy accounting for the degradation behavior of objects, then group interventions to minimize the net costs, considering dependencies within and accross infrastructure networks.We formulate the grouping problem as an Integer Linear Program, which can be solved exactly with standard solvers. The model accounts for interactions between infrastructure networks and considers the impact on all stakeholders. It also accommodates various intervention types like maintenance, removal, and upgrading.Using a demonstrative application, we show that our model significantly reduces net costs and outperforms alternative nonlinear formulations and related heuristics in terms of both solution quality and computation performance. Additionally, the optimal intervention plan shows repetitive patterns, which suggests that a rolling horizon strategy could be used where the optimization problem is solved for shorter time horizons, leading to significant computational benefits.

A preventive maintenance policy and a method to approximate the failure process for multi-component systems

Numerous maintenance policies have been proposed in the reliability mathematics and engineering literature. Nevertheless, little has been reported on their practical applications in industries. This gap is largely due to restrictive assumptions of the maintenance policies. Two of the main assumptions are that maintenance is conducted on typical components and that the reliability of an item under maintenance is known (where the item can be a component or a system composed of multiple components). These assumptions do not often hold in the real world: maintenance is often performed on a collection of components such as an integrated circuit plate and the reliability of each individual component may not be known. To reduce these gaps, this paper develops a new maintenance policy for a collection of components and an approximate method to estimate the reliability of this collection based on the failure data collected from the field. The maintenance policy considers that a system is composed of three subsystems with different levels of maintenance effectiveness (i.e, minimal, imperfect, and perfect). The approximate estimate of the reliability of each subsystem is derived based on the failure data that are time between failures of the system but not those of the components that cause the system to fail. An algorithm for simulating the superposition of generalised renewal processes is then proposed. Numerical examples are used to illustrate the proposed approximation method.

Preventive maintenance policy optimization for a weighted k-out-of-n: G system using the survival signature

This paper presents a novel preventive maintenance (PM) policy for a weighted k-out-of-n: G system. The system consists of critical components with high weight and other low-weight components, while the critical ones significantly impact the system's total weight. The lifetime distribution of critical and other components follows the Weibull distribution with different shape and scale parameters. Different maintenance actions are performed on the component according to the number of failed critical components and the total weight of the working components. The expected cost per renewal cycle and system availability are analyzed based on survival signature. The optimal PM cycle and the inspection period are derived by minimizing the expected cost per renewal cycle or maximizing availability. Finally, the effectiveness of the presented strategy is illustrated by a numerical case.

Pengendalian Kualitas Produk Gula Studi Kasus PT Madubaru PG. PS. Madukismo Yogyakarta

PG Madukismo is a sugar factory that was established in 1955 and is the only sugar factory in the province of the Special Region of Yogyakarta. This factory is located in Padokan Village, Tirtonirmolo Village, Kasihan District, Bantul Regency, Special Region of Yogyakarta. PT Madubaru, a company engaged in sugar cane processing, is a sugar and spirits factory located in the Yogyakarta area. Companies that produce cane sugar with SHS IA quality. Product defects are often found, resulting in a decrease in quality, therefore it is necessary to carry out quality control. This study uses the Failure Mode and Effect Analysis (FMEA) method to identify failure modes, Logic Tree Analysis (LTA) to determine the consequences of failure arising from failure modes and determine effective preventive maintenance policies for each machine. For the production quality control section, the Statistical Processing Control (SPC) method is used to analyze product defects to determine whether product defects are still within controllable limits or not. While the Failure Mode and Effect Analysis (FMEA) method is used to determine the failure mode in sugar production. For the quality control section on raw materials using the Economic Order Quantity (EOQ), Re-Order Point (ROP), Safety Stock (SS), and Total Inventory Cost (TIC) methods. The conclusion of this study is regarding the results of the analysis and calculations carried out with several settlement methods and there are several causes of production defects from 5 factors, such as: human factors, work methods, environment, raw materials and machines used. And provide solutions to companies in order to reduce defects by controlling product quality.

Optimal Preventive Maintenance Policy for Equipment Rented under Free Leasing as a Contributor to Sustainable Development

Leasing has proven to be a business model that is perfectly suited to the circular economy. It significantly contributes to sustainable development by enabling the reuse of machinery and equipment after each lease period and by including preventive maintenance and overhauls within and between lease terms. This helps to extend the life cycle of equipment, promote value recovery, and reduce waste. This paper examines an imperfect preventive maintenance (PM) strategy applied to equipment rented under the terms of “free leasing”. In free leasing, the lessor makes the equipment available to the customer for a specified period of time without charging rent. In return, the customer is required to purchase the equipment’s consumables exclusively from the lessor. The lessor is also responsible for the maintenance of the equipment at the customer’s premises. The greater the quantity of consumables used by the customer, the more the equipment will deteriorate. Consequently, the lessor must be able to determine the most effective approach to preventive maintenance, ensuring that it aligns with the customer’s planned usage rate while maximizing profit. This work proposes a PM strategy to be adopted by the lessor during the free lease period. This strategy involves the performance of imperfect PM actions just before the start of the lease period and then periodically. Different packages of preventive actions can be applied each time, with each package having a different cost depending on the level of effectiveness in terms of rejuvenating the equipment. Minimal repairs are performed in the event of equipment failure. The decision variables are the PM period to be adopted and the maintenance efficiency level to be chosen for each preventive intervention. The objective is to determine, for a given customer with an estimated consumption rate profile of consumables, the optimal values of these decision variables so that the lessor maximizes their profit. A mathematical model is developed to express the lessor’s average profit over each lease period. A solution procedure is developed for small instances of the problem, and an Artificial Bee Colony algorithm is implemented for larger instances. A numerical example and a sensitivity analysis are presented.

A study on two‐dimensional warranty with a preventive maintenance policy under burn‐in or run‐in

AbstractProducts are often offered with warranties, which have become a common marketing strategy intended to indicate that product quality is guaranteed by the firm. Warranties can attract consumer purchases, but they also incur warranty costs, especially when firms falsely claim that their products are high quality; therefore, the determination of an appropriate warranty policy is of particular importance. This study considers a two‐dimensional warranty for complex repairable products, such as a vehicle, a lathe, or industrial machinery, with consideration of (1) burn‐in (run‐in), which is a supervised accelerating testing process performed before product launch to prevent defective products from being sold on the market, and (2) periodic preventive maintenance, which is performed during the usage period to prevent the occurrence of unexpected failures under normal usage conditions. The optimal burn‐in (run‐in) time and preventive maintenance intervals are determined with the aim of minimizing warranty costs for firms. The results of the study show that given a higher repair cost and a greater infant mortality failure rate, more frequent preventive maintenance is preferable, and the repair cost has the largest effect on the total warranty cost for the firm.

Intelligent optimal preventive replacement maintenance policy for non-repairable systems

This paper developed optimal maintenance policies for industrial machines to ensure uninterrupted production. Analytical and intelligent replacement models were constructed using real-time data from a commercial photocopier operator’s expenditure logbook between 2012 and 2023. A goodness-of-fit test affirmed a log-logistic failure distribution with shape and scale parameters of 1.7233 and 763.9220, respectively. Probability functions from this distribution established replacement models, determining optimal preventive maintenance conditions. Through feature engineering additional features crucial for operational and cost optimization were abstracted, with 4,881 unique data points simulated from boundary conditions defined by the original or raw data. Analytical results showed that the failure distribution-based replacement model outperformed the hazard function-based model in minimum replacement time and unit maintenance cost over time, while key parameters such as availability, reliability, and failure occurrence probability remained constant at 96 %, 94 %, and 0.07 %, respectively. Machine learning models (Extreme Gradient Boosting: XGBoost, Support Vector Machine: SVM, and Multilayer Perceptron: MLP) demonstrated very high accuracy in availability (XGBoost = 99.49 %, SVM = 99.18 %, RF = 100 %, MLP = 99.80 %), reliability (XGBoost = 99.49 %, SVM = 99.18 %, RF = 100 %, MLP = 99.80 %), and maintenance cost (XGBoost = 100 %, SVM = 100 %, RF = 100 %, MLP = 100 %), exceeding results of the analytical models.

Preventive maintenance for coherent systems considering postponed replacement

AbstractOne primary objective of reliability engineering is to achieve optimal maintenance of technical systems, which ensures they remain in good operating condition. This paper proposes an age‐based preventive optimal maintenance policy for ‐component coherent systems. Under this proposed strategy, the system begins operating at and undergoes preventative maintenance (PM) at a time . If the system fails before , it will be replaced with a new one. If the system is still functioning at time , an assessment is made based on the number of failed components to determine whether the system should be replaced or allowed to continue operating. If the number of failures at is below a predetermined threshold , the PM time is postponed and a new PM time will be scheduled, and the system continues operating in the interval . Otherwise, the entire system is preventively replaced at with a new one. In this scenario, we use a cost function to determine the optimal values of decision variables , , and . To examine the effectiveness of our proposed model, we analyze some examples of coherent systems using graphical and numerical methods.

Calendar-time-based and age-based maintenance policies with different repair assumptions

Traditional maintenance models usually assume that the time duration required for conducting corrective repairs is negligible for mathematical tractability. In this paper, we propose two new preventive maintenance policies that take non-negligible repair time into consideration: One is a calendar-time-based maintenance policy in which a system should be preventively replaced at either of two predetermined calendar time instants, depending on the system state; and the other is an age-based maintenance policy in which a system should be preventively replaced at a predetermined age. Under both policies, if any failure occurs before replacement, then the system will be repaired. In particular, four repair assumptions—perfect, imperfect, minimal, and worse-than-minimal—that correspond to different repair effectiveness, are considered. We derive the long-run average cost rates for the two maintenance policies under different repair assumptions, respectively, and investigate the existence and uniqueness of the solutions of optimal maintenance decisions. Numerical studies are then conducted to compare the two maintenance policies, providing guidelines on which policy should be adopted by system operators in various scenarios.

An assessment of the effectiveness of the preventive maintenance policy in enhancing quality assurance in education provision: A case of selected primary schools in Chibombo District, Zambia

The purpose of this study was to investigate on an assessment of the effectiveness of the preventive maintenance policy in enhancing quality assurance in education provision: a case of selected schools in Chibombo district. The study employed a mixed paradigm and descriptive survey design that sampled DEBS officials, Head teachers, PMS coordinators, teachers and pupils. Data was obtained from respondents by means of interviews, questionnaires and observation checklist. The target population was 1280 respondents while the sample size was 128 respondents. Frequency, percentages, tables, graphs and pie-charts were used to analyze the quantitative and qualitative data obtained. Data was then analyzed manually in some cases and also, a combination of software MS Access and MS Excel. According to the findings, the study found that the activities which was in place was general cleaning one day per week. The study also found that discontinuity was the biggest challenge administrators experienced, and that many projects were abandoned. Unfortunately, the motto that "prevention is better than cure" was not evident among teachers and learners. The conclusion of this study with respect to the application of ethical theories to the findings is that the inner ethical motivation required for adequate care of property in teachers and learners is missing. The study therefore recommended that there is a need for greater emphasis to be placed on character formation in education as is specified in the Zambian Document Educating Our Future. Also, the spirit of the PMS policy directives needs to be more greatly emphasized in order to motivate action, rather than the more exclusive legal dimension which does not suffice to ensure the implementation of the policy.

Designing a two-dimensional maintenance policy for products sold under a two-dimensional warranty by considering both warranty servicing cost and lost opportunity

ABSTRACT Based on the manufacturer-consumer context, a two-dimensional preventive maintenance policy is developed for sold products under a two-dimensional warranty by pursuing two objectives: (1) minimization of the imposed warranty servicing cost on the manufacturer and (2) minimization of lost opportunity cost resulting from the product downtime. In addition, in order to adapt the model to real applications, we depart from the literature by taking into consideration the continuous nature of the usage rate variable. To illustrate the advantages of the model, three types of couple-based comparative models are made. Type I consists of two models with a one-dimensional preventive maintenance policy, while type II includes a model with no maintenance activity. Finally, type III involves two single-objective models. The results obtained from the comparison of the suggested model with the type I and II models confirm the significant effect of employing a two-dimensional preventive maintenance policy in improving both manufacturers’ and customers’ satisfaction. In addition, comparisons of the suggested bi-objective model with three types of models indicate the importance of taking into account both the warranty servicing cost and loss opportunity.

Preventive Maintenance Policy to Improve the Effectiveness of XYZ Machine

PT ABC which is a manufacturing company that produces steel pipes. The last period there was an XYZ machine that often downtime and disrupted the production process. The breakdowns indicate the ineffectiveness of machine. It was necessary to identify and analyze the problems that occurred and the factors that caused the problem. Measurement of machine effectiveness dan it caused is carried out using the OEE method. To avoid the recurrence of machine ineffectiveness, machine maintenance is carried out in a preventive manner according to the spare parts classification (there are 3 classifications of spare parts based on price, namely A under Rp.250,000, B between Rp.250,000 -Rp.500,000 and C above Rp.500,000). The results obtained for the average value of OEE for 1 year is 68%, which shows a value below the ideal OEE standard (85%) and needs to be evaluated. From the calculation of the largest losses value is in the reduce speed losses factor of 67%, it is necessary to minimize the difference in ideal cycle time with actual time (operation time). The best scheduling results is using the preventive maintenance policy method with the schedule of maintenance are: for classification A spare parts the maintenance every 3 months, class B every 5 months, and class C every 6 months.

EVALUATING THE MANAGEMENT AND MAINTENANCE PRACTICES OF LINEAR ACCELERATOR AND COBALT-60 CANCER TREATMENT MACHINES IN NIGERIA: A COMPREHENSIVE ASSESSMENT

This research paper investigated the state of radiotherapy machines and their management in cancer treatment centers in Nigeria. The study aimed to identify the challenges faced by these centers and propose strategies for improvement. Through a comprehensive analysis of data collected from surveys and questionnaires, critical issues impacting the availability, reliability, and effectiveness of radiotherapy machines were highlighted. The findings revealed a significant shortage of radiotherapy machines across the nation, with Nigeria having less than 6% of the required number of linacs. This scarcity led to a high workload on the existing machines, resulting in long waiting lists and compromised treatment outcomes. Management challenges, such as the absence of preventive maintenance policies, inadequate personnel training, delayed spare parts delivery, and limited engagement of maintenance contractors, were identified. The research emphasized the need for supportive cancer treatment machines, including orthovoltage and superficial machines, to alleviate the burden on existing radiotherapy machines. However, most centers lacked these supportive machines, limiting their ability to provide comprehensive care to patients. Financial sustainability also emerged as a significant concern, as internal funds proved insufficient for maintenance and operations. The involvement of the government and the establishment of independent regulatory bodies were proposed to address these challenges. In conclusion, this study called for urgent action to address the gaps and challenges in radiotherapy machine management in Nigeria. It highlighted the importance of increasing the number of machines, implementing preventive maintenance policies, enhancing staff training, and securing adequate funding. By doing so, the quality of cancer care could be significantly improved, leading to better patient outcomes and a more robust healthcare system in Nigeria.

Maintenance scheduling at high-speed train depots: An optimization approach

Under the preventive maintenance policy, high-speed trains need to conduct periodical inspection/overhaul at dedicated depots, where maintenance facilities and maintenance crews are available. Due to the complicated structure of trains, the maintenance work is rich in a variety of types, characterized by mechanical components repaired, maintenance levels and etc. In practice, these types of maintenance work are integrated into maintenance packets, and different packets may require the specific types of crews. In this paper, a mixed integer linear programming model is proposed for the depot maintenance packet assignment and crew scheduling problem. The objective of the problem is to minimize the overall crew worktime, while the main constraints include the compatibility between maintenance packets and crew types, maintenance packet duration time and execution order, maintenance time window, and crew worktime limit. Besides, two families of valid inequalities are proposed to improve the baseline model. Computational experiments on a set of randomly generated instances show the effectiveness and efficiency of the improved model compared to the baseline one. Finally, a real-world case study from Shanghai South Depot is carried out to further validate the proposed approach. Improvements on both solution time and quality are achieved in contrast with the manual schedule.

A copula-based approach to modelling the failure process of items under two-dimensional warranty and applications

Hundreds of scholarly papers on optimisation of preventive maintenance policies for items under warranty have been published in the reliability related literature. They typically have two limitations: they make a simplified assumption on the relationship between age and usage for items under two-dimensional warranty and they assume that it is cost-effective to conduct preventive maintenance (PM) on each sold product item. These assumptions may not reflect the reality. This paper therefore proposes a copula-based approach to modelling the relationship between age and usage of items under two-dimensional (2D) warranty. It investigates the probabilistic properties of the bivariate failure rate functions of age and usage and derives optimal PM policies for users who bought multiple product items. It also proposes approaches to incorporating maintenance effectiveness in bivariate copulas for depicting the dependence between age and usage. A case study and numerical examples are provided to illustrate the findings.

A novel shock-dependent preventive maintenance policy for degraded systems subject to dynamic environments and N-critical shocks

A novel shock-dependent preventive maintenance policy for degraded systems subject to dynamic environments and N-critical shocks

The impact of gamma usage processes on preventive maintenance policies under two-dimensional warranty

This study considers a manufacturer performing preventive maintenance (PM) on a product according to a one- or two-dimensional (2-D) policy. The one-dimensional PM policy is based on either time or usage, while in the two-dimensional case, PM is scheduled based on both scales. The product carries a 2-D warranty that offers protection for a certain amount of time and usage. Its cumulative usage is continuously monitored by the manufacturer and is assumed to follow a gamma process. In this context, we first propose a doubly stochastic Poisson process model for product failures where the stochastic intensity is influenced by the gamma usage process in an additive manner. We then explicitly derive the expected total costs of the two one-dimensional PM policies using the concepts of first hitting times and gamma bridges. For the 2-D PM policy, we express the associated cost in terms of the value function of a dynamic programming model. In the numerical experiments, we show how the variability of the usage process affects the costs of the three PM policies and find that the optimal 2-D policy degenerates into a one-dimensional policy.

Optimal condition-based maintenance policy for leased equipment considering hybrid preventive maintenance and periodic inspection

Innovations in technology have made the structure of leased equipment more complex, which poses a challenge for lessors to develop maintenance policies. In view of the situation that the single periodic preventive maintenance (PM) policy during leasing activities may cause excessive or insufficient maintenance on the leased equipment, a condition-based maintenance (CBM) policy considering hybrid preventive maintenance (HPM) and periodic inspections is proposed. This policy contains imperfect preventive maintenance (IPM), preventive replacement (PR) and corrective maintenance (CM). The lessor can decide to take maintenance actions depending on whether the state of the equipment reaches the corresponding maintenance threshold. The warranty period of the leased equipment is also considered. To seal the deal, the lessor will give a discount on the rental to cover the CM costs and penalties out of the warranty period. To minimize the lessor's costs, we propose a CBM decision model to determine the optimal inspection period, PM threshold and PR period. The results show that the HPM policy can reduce leasing costs by about 5 –15 % for the lessor compared to that of the single PM policy.