Hybrid Minimal Repair for a Serial System

This paper proposes, from the economical viewpoint of preventive maintenance in reliability theory, several preventive maintenance policies for an operating system that works for jobs at random times and is imperfectly maintained upon failure. As a failure occurs, the system suffers one of two types of failure based on a specific random mechanism: type-I (repairable) failure is rectified by a minimal repair, and type-II (non-repairable) failure is removed by a corrective replacement. First, a modified random and age replacement policy is considered in which the system is replaced at a planned time T, at a random working time, or at the first type-II failure, whichever occurs first. Next, as one extended model, the system may work continuously for N jobs with random working times. Finally, as another extended model, we might consider replacing an operating system at the first working time completion over a planned time T. For each policy, the optimal schedule of preventive replacement that minimizes the mean cost rate is presented analytically and discussed numerically. Because the framework and analysis are general, the proposed models extend several existing results.

Periodica Polytechnica Social and Management Sciences, 2022

The purpose of this article is to choose a maintenance procedure for the critical equipment of a forging production line with five machines. The research method is quantitative modelling and simulation. The main research technique includes retrieving time between failure and time to repair data and find the most likely distribution that has produced the data. The most likely failure rate function helps to define the maintenance strategy. The study includes two kinds of maintenance policies, reactive and anticipatory. Reactive policies include emergency and corrective procedures. Anticipatory policies include predictive and preventive ones combined with a total productive maintenance management approach. The most suitable combination for the first three machines is emergency and corrective choice. For the other machines, a combination of total productive maintenance and a predictive approach is optimal. The study encompasses the case of a serial production manufacturing line and maxi...

This paper presents a special case of integration of the preventive maintenance into the repair/replacement policy of a failure-prone system. The machine of the considered system exhibits increasing failure intensity and increasing repair times. To reduce the failure rate and subsequent repair times following a failure, there is an incentive to perform preventive maintenance on the machine before failure. When a failure occurs, the machine can be repaired or replaced by a new one. Thus the machine's mode at any time can be classified as either operating, in repair, in replacement or in preventive maintenance. The decision variables of the system are the repair/replacement switching age or number of failures at the time of the machine's failure and the preventive maintenance rate. The problem of determining the repair/replacement and preventive maintenance policies is formulated as a semi-Markov decision process and numerical methods are given in order to compute optimal policies which minimize the average cost incurred by preventive maintenance, repair and replacement over an infinite planning horizon. As expected, the decisions to repair or to replace the machine upon a failure are modified by performing preventive maintenance. A numerical example is given and a sensitivity analysis is performed to illustrate the proposed approach and to show the impact of various parameters on the control policies thus obtained.

International Journal of Production Research, 2020

The main objective of a maintenance policy consists of conducting maintenance actions at lower costs. This paper proposes an approach for comparing numerically three maintenance strategies, involving minimal repairs at failure, replacement with complete renewal only at the first failure, and replacement with complete renewal at each failure. These strategies are integrated into a modified block replacement policy that includes corrective and preventive maintenances. The approach proceeds by presenting the mathematical models at the component level and at the system level. As the renewal function for generalised Weibull distributions is impossible to obtain, a novel asymptotic algorithm is introduced for estimating the replacements number. However, a multi-component industrial example is proposed for selecting the strategy that minimises the maintenance costs. A sensitivity analysis is performed for comparing an opportunistic maintenance policy with the proposed replacement policy to check if substantial cost reduction still possible. The experiment results show clearly that the third strategy is the most efficient and reduces maintenance costs to a very low level. Finally, we think that the developed study provides a flexible and less costly solution to deal with maintenance decision-making for systems that do not have modern technological equipment to collect data from system breakdowns.

This paper deals with a hybrid minimal repair and age replacement policy after the expiry of warranty for a repairable product sold with a non-renewing failure replacement warranty. Parameters of such policy are defined as 0 T and T which indicate as minimally repair time and planned replacement time respectively. Under the hybrid policy, the product with one dimensional warranty W is repaired minimally when it fails during [0,T0 ] and with a failure replacement on first failure after 0 T or a planned replacement at time T , whichever occurs first. For a givenT , we obtain the optimal value 0 T which minimizes the expected cost per unit time to the buyer. A numerical example is given to illustrate the properties of the optimal solution. Keywords: Hybrid maintenance policy, One-dimensional warranty, the expected cost per unit time.

2002

With the evolution of technology, the maintenance of sophisticated systems is of concern for system engineers and system designers. The maintenance cost of the system depends in general on the replacement and repair policies. The system replacement may be in a strictly periodic fashion or on a random basis depending upon the maintenance policy. At failure, the repair of the system may be performed perfectly or minimally associated with some probability. When perfect repair is done, it makes the system as good as the new one. In case of minimal repair, it returns to the working condition of the system at the time of failure. In the present paper, we study the replacement policies for the system wherein minimal or perfect repair is done at the time of failure. The expressions for s- expected cost for the system with replacement and minimal or perfect repair are evaluated. The maintenance costs are discussed for various policies. Numerical simulation is performed to validate the analyt...

System Reliability, 2017

This chapter investigates optimization of maintenance policy of a repairable equipment whose lifetime distribution depends on the operating environment severity. The considered equipment is undergone to a maintenance policy which consists of repairing minimally at failure and maintaining after operating periods. The periodic maintenance is preventive maintenance (PM) and allows reducing consequently the equipment age but with higher cost than minimal repair. In addition, the equipment has to operate at least in two operating environments with different severity. Therefore, in this analysis, the equipment lifetime distribution function depends on the operating severity. Under these hypotheses, a mathematical modeling of the maintenance cost per unit of time is proposed and discussed. This cost is mathematically analyzed in order to derive optimal periods between preventive maintenance (PM) and the optimal condition under which these exist.

In this paper, we investigate a hybrid minimal repair and age replacement policy for a repairable product sold with a two-dimensional non-renewing failure replacement warranty (NFRW). Modeling failure incorporates usage pattern of the product. For a given usage rate y, the hybrid policy is divided into two periods i.e. (0, S y ] and (S y , τ y ]. Under this policy, for a given usage rate y, the product is repaired minimally when it fails in (0, S y ] and it is replaced with a new one on first failure in (S y , τ y ] or when its age reaches y  , whichever occurs first. We obtain the global optimal value S y , for a given τ y which minimizes the expected cost per unit time to the buyer. We present numerical examples to illustrate the properties of the optimal solution.

Naval Research Logistics Quarterly

This paper is a state-of-the-art review of the literature related to optimal maintenance models of systems subject to failure. The emphasis is on work appearing since the 1976 survey, "A Survey of Maintenance Models: The Control and Surveillance of Deteriorating Systems," by W.P. Pierskalla and J.A. Voelker, published in this journal. 0 If individual parts cannot be considered as stochastically and economically independent, then a policy called the opportunistic maintenance policy will be more effective. Under this policy, the maintenance of a single uninspected part depends on the state of one or more continuously inspected parts. The opportunistic maintenance policy is advantageous when the cost of a joint maintenance action is less than the sum of the cost of the separate maintenance actions. If a complex system is composed of a large collection of identical units of equipment, then a block maintenance policy may be advantageous. Under this policy, each unit is replaced on failure, and all units are replaced at periodic intervals, T, 2T, 3T,. .. , without regard to individual unit age. Scheduled and unscheduled maintenance can be combined. Consequently, this policy is easier to implement, and results in lower administrative and maintenance costs. 2.2.4 Complex System Preparedness Maintenance Model (periodic, sequential, opportunistic) This model is an extension of 2.2.2 for complex systems. The optimal policy for various assumptions is as follows: 0 If the complex system is under continuous surveillance, then this model reduces to the preventive maintenance model described under 2.2.3. If the complex system is not inspected, then the only maintenance policy to secure the highest level of preparedness is replacement. 2.3 Stochastic Models Under Uncertainty For stochastically failing equipment under uncertainty, the exact time of failure and the distribution of the time to failure are not known. 2.3.1 Preventive Maintenance Model for Simple and Complex Systems The optimal policy for various assumptions is obtained as follows: When the system is new or failure data are not known, 0 When information about the system (failure rate,. .. , 0 When subjective beliefs about the system failure are known, the minimax techniques are applied. etc.) is partially known, Chebyshev-type bounds are applied. Bayesian adaptive techniques are applied. 2.3.2 Simple (complex) System Preparedness Maintenance Model The techniques of minimax strategies, Chebyshev-type bounds and Bayesian adaptive policies can be applied to this model as explained under item 2.3.1.

Journal of Quality in Maintenance Engineering, 2011

PurposeThis paper aims to investigate the optimization of the replacement with minimal repair policy for a system which experiences a time horizon of random length. Under such policy system replacement occurs at multiples of some period while minimal repair is performed at system failure between two successive replacements.Design/methodology/approachThe objective function is the expected total cost composed of minimal repairs and replacements costs. A simple and compact expression is derived for the expected total costs and conditions under which an optimal replacement period exits are given. For sake of illustration, a numerical example is provided.FindingsThe paper finds that by the recent great technological development, the life cycle of present products is seen to be reduced more and more. This has motivated the development of maintenance optimization models for systems which experience an exact finite time horizon.Originality/valueTo ensure the benefits from the improved techn...