Preventive Maintenance Cost Research Articles

Discriminant Analysis Based Prognostics of Avionic Systems

In this paper, a novel discriminant analysis based predictive model for preventing false alarms leading to unnecessary replacement of an avionic system component is presented. The model is validated by prediction of false alarms (also known as false positives, type I, or alpha errors) in the left generator shaft of a Sikorsky helicopter UH-60, using the Goodrich health and usage management system (HUMS). The paper presents one of the first approaches based on applying discriminant analysis for prognostics of avionic systems, specifically in the context of identifying false positives within the next 1 or 2 h. In practice, predictions for the next 2 h are sufficient as typical helicopter flight schedules and durations are such that up to 2 h advance notice is most useful. This is an important contribution because drive train components of helicopters are normally very robust with very rare failures; therefore, the cost of unnecessary preventive maintenance based on false alarms is very high.

Adaptive Sequential Preventive Maintenance Policy and Bayesian Consideration

This article proposes an adaptive sequential preventive maintenance (PM) policy for which an improvement factor is newly introduced to measure the PM effect at each PM. For this model, the PM actions are conducted at different time intervals so that an adaptive method needs to be utilized to determine the optimal PM times minimizing the expected cost rate per unit time. At each PM, the hazard rate is reduced by an amount affected by the improvement factor which depends on the number of PM's preceding the current one. We derive mathematical formulas to evaluate the expected cost rate per unit time by incorporating the PM cost, repair cost, and replacement cost. Assuming that the failure times follow a Weibull distribution, we propose an optimal sequential PM policy by minimizing the expected cost rate. Furthermore, we consider Bayesian aspects for the sequential PM policy to discuss its optimality. The effect of some parameters and the functional forms of improvement factor on the optimal PM policy is measured numerically by sensibility analysis and some numerical examples are presented for illustrative purposes.

Age-dependent production planning and maintenance strategies in unreliable manufacturing systems with lost sale

In this paper, we formulate an analytical model for the joint determination of an optimal age-dependent buffer inventory and preventive maintenance policy in a production environment that is subject to random machine breakdowns. Traditional preventive maintenance policies, such as age and periodic replacements, are usually studied based on simplified and non-realistic assumptions, as well as on the expected costs criterion. Finished goods inventories and the age-dependent likelihood of machine breakdowns are usually not considered. As a result, these policies could significantly extend beyond the anticipated financial incomes of the system, and lead to crises. In order to solve this problem, a more realistic analysis model is proposed in this paper to consider the effects of both preventive maintenance policies and machine age on optimal safety stock levels. Hence, a unified framework is developed, allowing production and preventive maintenance to be jointly considered. We use an age-dependent optimization model based on the minimization of an overall cost function, including inventory holdings, lost sales, preventive and corrective maintenance costs. We provide optimality conditions for the manufacturing systems considered, and use numerical methods to obtain an optimal preventive maintenance policy and the relevant age-dependent threshold level production policy. In this work, this policy is called the multiple threshold levels hedging point policy. We include numerical examples and sensitivity analyses to illustrate the importance and the effectiveness of the proposed methodology. Compared with other available optimal production and maintenance policies, the numerical solution obtained shows that the proposed age-dependent optimal production and maintenance policies significantly reduce the overall cost incurred.

Predictive condition‐based maintenance for continuously deteriorating systems

AbstractThis paper addresses a predictive condition‐based maintenance approach based on monitoring, modeling, and predicting a system's deterioration. The system's deterioration is considered as a stochastic dynamic process with continuous degrading. Structural time series, coupled with state‐space modeling and Kalman filtering methods, is adopted for recursively modeling and forecasting the deterioration state at a future time. The probability of a failure is then predicted based on the forecasted deterioration state and a threshold of a failure. Finally, maintenance decisions are made according to the predicted failure probabilities, associated preventive and corrective maintenance cost, and the profit loss due to system performance deterioration. The approach can be applied on‐line to provide economic and preventive maintenance solutions in order to maximize the profit of the ownership of a system. Copyright © 2007 John Wiley & Sons, Ltd.

Optimal safety stocks and preventive maintenance periods in unreliable manufacturing systems

We consider a manufacturing system with preventive maintenance that produces a single part type. An inventory is maintained according to a machine-age-dependant hedging point policy. We conjecture that, for such a system, the failure frequencies can be reduced through preventive maintenance resulting in possible increase in system performance. Traditional preventive maintenance policies, such as age replacement, periodic replacement, are usually studied without finished goods inventories. In the cases where the finished goods inventories are considered, restrictive assumptions are used, such as not allowing breakdown during the stock build up period and during backlog situations due to the complexity of the mathematical model. In order to solve this problem, we develop a more realistic mathematical model of the system, and derive expressions of the overall incurred cost used as the basis for optimal determination of the jointly production and preventive maintenance policies (i.e. production rates and preventive maintenance frequency, depending on inventory levels of the produced parts). Such a cost consists of inventory, backlog, corrective and preventive maintenance costs. The work reported here has a significant practical application (no restriction on failures occurrence and backlog situations) in the context of production planning of manufacturing systems. Numerical examples are included to illustrate the importance and the effectiveness of the proposed methodology.

An approach for evaluating preventive maintenance scheduling cost

PurposeThis paper aims to revisit the preventive maintenance scheduling literature. The problem to be solved is the simultaneous scheduling of resource‐constrained preventive maintenance and operations. In particular, the expression that defines the period‐dependent cost function for a preventive maintenance scheduling activity is redefined. A case study is presented from the shipping industry.Design/methodology/approachIn this paper a mathematical theory of differential calculus known as three‐dimensional wave equation is applied. The methodology involves transforming the preventive maintenance cost function that is expressed in terms of several variables into a more precise framework. The motivation for the work is the need to measure the total preventive maintenance scheduling cost more precisely than with the use of the existing linear cost structure.FindingsIn this paper the findings from the analysis carried out found evidence that validates the claim of the feasibility of analyzing preventive maintenance cost using the approach proposed.Research limitations/implicationsThe paper shows that, in practice, maintenance managers strive to reduce the cost of preventive maintenance activities in order to achieve low cost production of goods. This would encourage a high patronage of customers and prevent decisions being made on wrong data. The approach presented here aims at correcting this weakness by revealing a more precise and reliable method of preventive maintenance scheduling cost computation. This is a scientific tool that should be of immense benefit to maintenance planners, particularly those actively engaged in scheduling functions.Originality/valueThe work in this paper is new, since a novel framework is presented in a way that has not been documented earlier.

Reducing the cost of preventive maintenance (PM) through adopting a proactive reliability-focused culture

The economic and political realities of the 1990s forced managers to reverse long-standing organizational cultures in order to reduce costs and energy expenditures in their organisations. For instance, these can be achieved, with respect to maintenance, by replacing a reactive repair-focused attitude by a proactive reliability-focused culture. Thereby far less (i) human effort is expended and (ii) energy would be wasted, both of which lead to increased profitability.

Discounted costs for repairable units under hybrid warranty

In the present investigation, we propose a hybrid warranty model for the renewing pro-rata warranty (RPRW), in which the failure rate of units, cost of preventive maintenance and cost of replacement are assumed to be constant. The expected total discounted maintenance cost has been derived in explicit form for different life time distributions viz. exponential, Rayleigh and Weibull distributions. We determine the optimal number and optimal period for preventive maintenance after the expiry of the warranty. Numerical illustrations are provided to check the validity of the analytical results. ts.

A model for preventive maintenance planning by genetic algorithms based in cost and reliability

This work has two important goals. The first one is to present a novel methodology for preventive maintenance policy evaluation based upon a cost-reliability model, which allows the use of flexible intervals between maintenance interventions. Such innovative features represents an advantage over the traditional methodologies as it allows a continuous fitting of the schedules in order to better deal with the components failure rates. The second goal is to automatically optimize the preventive maintenance policies, considering the proposed methodology for systems evaluation. Due to the great amount of parameters to be analyzed and their strong and non-linear interdependencies, the search for the optimum combination of these parameters is a very hard task when dealing with optimizations schedules. For these reasons, genetic algorithms (GA) may be an appropriate optimization technique to be used. The GA will search for the optimum maintenance policy considering several relevant features such as: (i) the probability of needing a repair (corrective maintenance), (ii) the cost of such repair, (iii) typical outage times, (iv) preventive maintenance costs, (v) the impact of the maintenance in the systems reliability as a whole, (vi) probability of imperfect maintenance, etc. In order to evaluate the proposed methodology, the High Pressure Injection System (HPIS) of a typical 4-loop PWR was used as a case study. The results obtained by this methodology outline its good performance, allowing specific analysis on the weighting factors of the objective function.

New methods to minimize the preventive maintenance cost of series–parallel systems using ant colony optimization

This article is based on a previous study made by Bris, Châtelet and Yalaoui [Bris R, Chatelet E, Yalaoui F. New method to minimise the preventive maintenance cost of series–parallel systems. Reliab Eng Syst Saf 2003;82:247–55]. They use genetic algorithm to minimize preventive maintenance cost problem for the series–parallel systems. We propose to improve their results developing a new method based on another technique, the Ant Colony Optimization (ACO). The resolution consists in determining the solution vector of system component inspection periods, TP. Those calculations were applied within the programming tool Matlab. Thus, highly interesting results and improvements of previous studies were obtained.

Optimal Preventive Maintenance Scheduling in Semiconductor Manufacturing

Preventive maintenance (PM) scheduling is a very challenging task in semiconductor manufacturing due to the complexity of highly integrated fab tools and systems, the interdependence between PM tasks, and the balancing of work-in-process (WIP) with demand/throughput requirements. In this paper, we propose a two-level hierarchical modeling framework. At the higher level is a model for long-term planning, and at the lower level is a model for short-term PM scheduling. Solving the lower level problem is the focus of this paper. We develop mixed-integer programming (MIP) models for scheduling all due PM tasks for a group of tools, over a planning horizon. Interdependence among different PM tasks, production planning data such as projected WIP levels, manpower constraints, and associated PM time windows and costs, are incorporated in the model. Results of a simulation study comparing the performance of the model-based PM schedule with that of a baseline reference schedule are also presented.

Warranty and discrete preventive maintenance

Preventive maintenance actions over the warranty period have an impact on the warranty servicing cost to the manufacturer and the cost to the buyer of fixing failures over the life of the product after the warranty expires. However, preventive maintenance costs money and is worthwhile only when these costs exceed the reduction in other costs. The paper deals with a model to determine when preventive maintenance actions (which rejuvenate the unit) carried out at discrete time instants over the warranty period are worthwhile. The cost of preventive maintenance is borne by the buyer. (C) 2003 Elsevier Ltd. All rights reserved.

Optimal Predictive Maintenance Policies for a Deteriorating System: The Total Discounted Cost and the Long-Run Average Cost Cases

A predictive maintenance policy for a system subject to degradation is considered. Over time, the system is subject to degradation. The degradation level is observed only at successive inspection times. The system has a nominal life Y, and it fails once the degradation exceeds the nominal life. Failure is detected only by inspection, and is then maintained (corrective maintenance). The system is also maintained when the degradation level exceeds a predetermine level M(preventive maintenance). Each preventive maintenance costs c 1, while the cost of a corrective maintenance is equal to c 1 + d, d ≥ 0. At any inspection, before failure, the cost of inspection is state dependent: if the degradation level at inspection is x, then an inspection cost of c(x) occurs. We determine the optimal maintenance level M using the total discounted cost as well as the long-run average cost criteria. Our results extend those of Grall et al. [Grall, A., Dieulle, L., Berenguer, C., Roussignol, M. (2002). Continuous-time predictive-maintenance scheduling for a deteriorating system. IEEE Trans. Reliability 51:141–149].

New method to minimize the preventive maintenance cost of series–parallel systems

General preventive maintenance model for input components of a system, which improves the reliability to ‘as good as new,’ was used to optimize the maintenance cost. The cost function of a maintenance policy was minimized under given availability constraint. An algorithm for first inspection vector of times was described and used on selected system example. A special ratio-criterion, based on the time dependent Birnbaum importance factor, was used to generate the ordered sequence of first inspection times. Basic system availability calculations of the paper were done by using simulation approach with parallel simulation algorithm for availability analysis. These calculations, based on direct Monte Carlo technique, were applied within the programming tool Matlab. A genetic algorithm optimization technique was used and briefly described to create the Matlab's algorithm to solve the problem of finding the best maintenance policy with a given restriction. Adjacent problem, which we called ‘reliability assurance,’ was also theoretically solved, concerning the increase of the cost when asymptotic availability value conforms to a given availability constraint.

Maintenance strategy for tilting table of rolling mill based on reliability considerations

Abstract Reliability centred maintenance (RCM) is a new strategic framework for ensuring that any asset continues to perform, as its users want it to perform. RCM is a process used to determine the maintenance requirement of any physical asset in its operating context. RCM process entails asking seven questions about each of the selected assets. It makes use of two documents namely, RCM information worksheet and RCM decision worksheet. RCM decision diagram integrates all the decision processes into a single strategic framework. RCM concept developed by US commercial airlines industry has been successfully implemented by Military, Navy, Nuclear power plants, electric power generation and distribution undertakings and several other sectors. These projects have been carried out in the United Kingdom, The Republic of Ireland, the United States, Hong Kong, Australia, Spain and Singapore. The fact that people has enthusiastically received RCM at all levels and has enabled users to achieve some remarkable successes in all of these countries, suggests that it can be universally employed. Literature review indicates that RCM approach is not conventionally applied in process industries in India. Presently, predictive maintenance (PDM) approach along with conventional preventive maintenance is used in continuous/process industries. This approach if implemented in totality will increase the production cost to a large degree and make the production uneconomical. Similarly breakdown maintenance (BDM) approach cannot be applied in such industries as each breakdown involves huge costs. RCM approach is a compromise between PDM and BDM approach for optimising the cost and ensuring the availability of machine. The RCM approach has been applied to the tilting table system of rolling mill for the research work reported in this paper. In the present study, preventive maintenance tasks suggested for power transmission subsystem, guiding and transportation subsystem and hydraulic subsystem in tilting tables are 14 scheduled on-condition tasks, 10 scheduled on-restoration tasks, seven scheduled discard task. Whereas for 14 failure modes no scheduled maintenance has been proposed. Existing maintenance schedule for tilting tables indicates the maintenance action as and when required. Hence RCM based schedule specifies that additional preventive maintenance tasks need to be executed as compared to none initially. Cost incurred for this can be offset from the savings accrued from reduction in loss of production due to repetitive breakdowns. The methodology of RCM adopted in western industries cannot be applied as it is to Indian industries because of labour oriented nature, partially computerised information systems, non-availability of the information about cost of loss of production due to breakdown and age-reliability pattern of equipment, insufficient maintenance database. These problems can be overcome by development of sound MMIS, formulation of RCM review group and imparting suitable training to acquire the relevant skills in RCM. Thus RCM methodology can be applied to Indian industry for reduction of breakdowns as well as optimisation of preventive maintenance cost. This can further boost up the prospects of Indian industry to offer the products at globally competitive prices.

蒸気タービンにおけるフィールドデータの信頼性解析に基づくリスク評価法

By arranging the field maintenance database of steam turbines with operation history and by conducting probabilistic safety analyses, a quantitative risk assessment method was established. The method includes three steps: (1) event tree analyses of parts failures which require any maintenance action, (2) assignment of unreliability functions of operation time or start cycles for each item in those event trees, and (3) calculation of total risk function by summing up recovery costs multiplied by the unreliability functions obeying the event trees and comparison of the risk cost with preventive maintenance cost. Those three steps were applied to actual turbine components and the effectiveness of proposed method was demonstrated with a risk simulation analysis.

The economic production lot-sizing problem with imperfect production processes and imperfect maintenance

This paper deals with an integrated model for the joint determination of economic production quantity and preventive maintenance (PM) level for an imperfect process having a general deterioration distribution with increasing hazard rate. The effect of PM activities on the deterioration pattern of the process is modeled using the imperfect maintenance concept. It is assumed that, after each PM, the age of the system is reduced proportional to the PM level. A numerical example is used to illustrate the effect of PM level on the cost of nonconforming items and restoration cost due to shift to the out-of-control state. It is found that performing PM will yield reductions in quality related costs. It is more economical to perform PM when the extra PM cost is compensated for by the reduction in quality related costs.

629 蒸気タービンのリスクベースメンテナンスシステム

A risk-based maintenance system was established to improve economical benefits of maintenance based on probabilistic risk assessment. The system has four subsystems for field data management, for probabilistic life assessment, for probabilistic risk assessment and for maintenance planing. Unreliability functions of operation hours or number of starts were derived from the reliability analysis of field inspection data and the life assessment using statistical material data. The risk was calculated by summing up the recovery costs expected by the unreliability along event trees. The maintenance planning was conducted by comparing operation benefits, preventive maintenance costs and risk costs. This system can be applied to various types of steam turbines by using the unified unreliability functions expressed by life or event governing parameters.

蒸気タービンのリスクを考慮した寿命予測と保守管理法

Reliability analyses were conducted on field inspection data of steam turbines and creep data of turbine rotor materials to show how risk-based maintenance was introduced into the life assessment and maintenance of steam turbine plants. The cumulative hazard function method was applied to the field events requiring recovery actions, in order to obtain unreliability functions of operation hours or start cycles. The rotor bowing phenomena were investigated as the typical examples of creep deformation. The event tree and unreliability functions were obtained for those phenomena. Moreover, a unified unreliability function was obtained by substituting operation hours to time fraction t⁄tr, where, tr was time to rupture. As the time to rupture was expressed by the function of stress, temperature and Vickers hardness, this unified unreliability function could be used for various machine and material heats. Unreliability functions were also obtained from the statistical analysis of creep rupture data, especially using the normalization of stress by hardness. The risk functions were derived from the sum of unreliability functions multiplied by recovery costs along the event tree. Maintenance intervals could be determined based on cost analyses among risk costs, preventive maintenance costs and operational benefits. This procedure was proved to be effective for economical and reliable operation & maintenance for steam turbine plants.

Management of industrial maintenance – economic evaluation of maintenance policies

One of the main problems, when choosing between preventive or corrective maintenance for the production and maintenance departments in the eight firms studied, is to attempt to establish the state of a particular production system or individual production line. In order to carry out a cost analysis it is imperative to be able to measure how preventive maintenance will both reduce the deterioration of the state of the object and improve the state of the object at the point in time that the maintenance is carried out. This can be explained by the fact that the departments lack methods for measuring and estimating the effects. The model presented in this paper determines whether to schedule preventive maintenance and the model trades off the capital costs of preventive maintenance and the sum of corrective maintenance and down‐time costs based on the production line’s state.