Stochastic Models in Preventive Maintenance Policies

2014 ◽  
Vol 1016 ◽  
pp. 802-806
Author(s):  
Onur Gölbaşı ◽  
Nuray Demirel
Keyword(s):  
Poisson Process ◽  
Stochastic Models ◽  
Preventive Maintenance ◽  
Operating Cost ◽  
Replacement Policy ◽  
Market Demand ◽  
Imperfect Maintenance ◽  
Maintenance Policy ◽  
Maintenance Policies ◽  
Age Replacement

In recent decades, philosophy behind maintenance has varied consistently due to the changes in complexity of designs, advances in automation and mechanization, adaptation to the fast growing market demand, commercial computation in the sectors, and environmental issues. In mid-forties, simplicity of designs, limited maintenance opportunities, and immaturity of trade culture made enough to performonly fix it when it brokeapproach, i.e. corrective maintenance, after failures. Last quarter of the 21thcentury made essential to constitute more conservative and preventive maintenance policies in order to ensure safety, reliability, and availability of systems with longer lifetime and cost effectiveness. Preventive maintenance can provide an economic saving more than 18% of operating cost of systems. In this basis, various stochastic models were proposed as a tool to constitute a maintenance policy to measure system availability and to obtain optimal maintenance periods. This paper presents a general perspective on common stochastic models in maintenance planning such as Homogenous Poisson Process, Non-Homogenous Poisson Process, and Imperfect Maintenance. The paper also introduces two common maintenance policies, block and age replacement policy, using these stochastic models.

Analysis of Spare Parts Demand Forecasting Considering Preventive Maintenance

2013 ◽  
Vol 401-403 ◽  
pp. 2199-2204 ◽  
Author(s):  
Hao Liu ◽  
Jian Min Zhao ◽  
Jin Song Zhao ◽  
Hong Zhi Teng
Keyword(s):  
Weibull Distribution ◽  
Preventive Maintenance ◽  
Empirical Formula ◽  
Demand Forecasting ◽  
Spare Parts ◽  
Replacement Policy ◽  
Maintenance Policy ◽  
Reliability Engineering ◽  
Demand Rate ◽  
Age Replacement

Considering the importance of PM (preventive maintenance) in reliability engineering, the formula is given to calculate spare demand rate for the policies of age replacement policy, minimal maintenance policy and block replacement policy. And average spare demand rate was analyzed for age replacement policy, and an approximate empirical formula with PM interval and parameters of Weibull distribution was given compared to CM(corrective maintenance) and PM. Otherwise, compared to minimal maintenance policy and block replacement policy, the demand rate was analyzed in order to better forecast the spare parts demand.

Availability analysis and maintenance optimization for multiple failure mode systems considering imperfect repair

2021 ◽  
pp. 1748006X2110127
Author(s):  
Qingan Qiu ◽  
Baoliang Liu ◽  
Cong Lin ◽  
Jingjing Wang
Keyword(s):  
Failure Modes ◽  
Maintenance Cost ◽  
Imperfect Maintenance ◽  
Maintenance Policy ◽  
Cost Rate ◽  
Availability Analysis ◽  
Long Run ◽  
Optimal Maintenance ◽  
Maintenance Policies ◽  
Periodic Inspections

This paper studies the availability and optimal maintenance policies for systems subject to competing failure modes under continuous and periodic inspections. The repair time distribution and maintenance cost are both dependent on the failure modes. We investigate the instantaneous availability and the steady state availability of the system maintained through several imperfect repairs before a replacement is allowed. Analytical expressions for system availability under continuous and periodic inspections are derived respectively. The availability models are then utilized to obtain the optimal inspection and imperfect maintenance policy that minimizes the average long-run cost rate. A numerical example for Remote Power Feeding System is presented to demonstrate the application of the developed approach.

A cost relationship between age and block replacement policies

1988 ◽  
Vol 25 (04) ◽  
pp. 789-796 ◽  
Author(s):  
Thomas H. Savits
Keyword(s):  
Stochastic Process ◽  
Operating Cost ◽  
Cost Structure ◽  
Renewal Function ◽  
Maintenance Policy ◽  
Long Run ◽  
On Line ◽  
Age Replacement ◽  
Operating Unit

The general cost structure of a unit on line is assumed to be governed by a stochastic process , where R(t) denotes the operating cost on [0, t)and ζ denotes the time of an unscheduled (or unplanned) replacement by a new unit at a cost c 1. For an age replacement maintenance policy, scheduled (or planned) replacements occur whenever an operating unit reaches age T, whereas in the block replacement case, scheduled replacements occur every T units of time. Such scheduled replacements cost c2. The expected long-run cost per unit time can then be expressed in the form A(T)/Ε [min(ζ, T)] and B(T)/T respectively. Our main result shows that where U is the associated renewal function generated by ζ.

A hybrid preventive maintenance model for systems with partially observable degradation

2020 ◽  
Vol 31 (3) ◽  
pp. 345-365 ◽  
Author(s):  
Maxim Finkelstein ◽  
Ji Hwan Cha ◽  
Gregory Levitin
Keyword(s):  
Preventive Maintenance ◽  
Replacement Policy ◽  
Hybrid Strategy ◽  
Cost Rate ◽  
Shot Noise Process ◽  
Shock Process ◽  
Long Run ◽  
Age Replacement ◽  
Partially Observable ◽  
Maintenance Model

Abstract A new model of hybrid preventive maintenance of systems with partially observable degradation is developed. This model combines condition-based maintenance with age replacement maintenance in the proposed, specific way. A system, subject to a shock process, is replaced on failure or at some time ${T}_S$ if the number of shocks experienced by this time is greater than or equal to m or at time $T>{T}_S$ otherwise, whichever occurs first. Each shock increases the failure rate of the system at the random time of its occurrence, thus forming a corresponding shot-noise process. The real deterioration of the system is partially observed via observation of the shock process at time ${T}_S$. The corresponding optimization problem is solved and a detailed numerical example demonstrates that the long-run cost rate for the proposed optimal hybrid strategy is smaller than that for the standard optimal age replacement policy.

On preventive maintenance policies: a selection framework

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Imad Alsyouf ◽  
Sadeque Hamdan ◽  
Mohammad Shamsuzzaman ◽  
Salah Haridy ◽  
Iyad Alawaysheh
Keyword(s):  
Preventive Maintenance ◽  
Fuzzy Ahp ◽  
Critical Component ◽  
Multi Objective Optimization ◽  
Maintenance Policy ◽  
Coolant Pump ◽  
Content Type ◽  
Multi Objective ◽  
Maintenance Policies ◽  
Preventive Maintenance Policy

PurposeThis paper develops a framework for selecting the most efficient and effective preventive maintenance policy using multiple-criteria decision making and multi-objective optimization.Design/methodology/approachThe critical component is identified with a list of maintenance policies, and then its failure data are collected and the optimization objective functions are defined. Fuzzy AHP is used to prioritize each objective based on the experts' questionnaire. Weighted comprehensive criterion method is used to solve the multi-objective models for each policy. Finally, the effectiveness and efficiency are calculated to select the best maintenance policy.FindingsFor a fleet of buses in hot climate environment where coolant pump is identified as the most critical component, it was found that block-GAN policy is the most efficient and effective one with a 10.24% of cost saving and 0.34 expected number of failures per cycle compared to age policy and block-BAO policy.Research limitations/implicationsOnly three maintenance policies are compared and studied. Other maintenance policies can also be considered in future.Practical implicationsThe proposed methodology is implemented in UAE for selecting a maintenance scheme for a critical component in a fleet of buses. It can be validated later in other Gulf countries.Originality/valueThis research lays a solid foundation for selecting the most efficient and effective preventive maintenance policy for different applications and sectors using MCDM and multi-objective optimization to improve reliability and avoid economic loss.

scholarly journals Integration of maintenance systems

2018 ◽  
Vol 211 ◽  
pp. 03010
Author(s):  
S H Sarje
Keyword(s):  
Preventive Maintenance ◽  
Hazard Rate ◽  
Reduction Factor ◽  
Maintenance Cost ◽  
Cost Ratio ◽  
Imperfect Maintenance ◽  
Maintenance Policy ◽  
Maintenance System ◽  
Reliability Centered Maintenance ◽  
High Flexibility

Excellence in maintenance is imperative in highly competitive market because it resulted into minimum maintenance cost, high equipment effectiveness, maximum reliability of the system, high quality of the products, low delivery time, high flexibility, safety etc. Any maintenance system such as Total Productive Maintenance (TPM) or Reliability Centered Maintenance (RCM) or Condition Based Maintenance (CBM) alone cannot achieve the excellence in maintenance but its integration may do. In this paper, an integration of TPM, RCM and CBM is proposed with a maintenance policy to take advantage of their respective strengths. A continuously monitored system subject to degradation due to the imperfect maintenance, where a hybrid hazard rate based on the concept of age reduction factor and hazard rate increase factor to predict the evolution of the system reliability in different maintenance cycles has been assumed.A quantitative decision making model for an integrated maintenance system is derived in order to assess the performance of the proposed maintenance policy. Numerical examples of calculation of optimal preventive maintenance age x and preventive maintenance number N* for the given cost ratio of corrective replacement and predictive preventive maintenance are given.

OPTIMAL RANDOM AGE REPLACEMENT FOR AVAILABILITY

2012 ◽  
Vol 19 (05) ◽  
pp. 1250021 ◽  
Author(s):  
JOHN E. ANGUS ◽  
MENG-LAI YIN ◽  
KISHOR TRIVEDI
Keyword(s):  
Preventive Maintenance ◽  
Sufficient Conditions ◽  
Optimal Rate ◽  
Maintenance Policy ◽  
Design Decisions ◽  
System Availability ◽  
Critical Design ◽  
Long Run ◽  
Age Replacement

An age replacement maintenance policy is considered here, in which a system is restored whenever it fails, or ages without failure up to a preventive maintenance epoch (whichever comes first). The duration of the restoration activity is random, and depends on whether it was precipitated by a failure or by a preventive maintenance action. The case where the preventive maintenance epoch is deterministic has been studied previously, and shown to be optimal in a certain sense. Here, we consider the case where the preventive maintenance epoch is randomized, which is more realistic for many systems. The system availability is the long run proportion of time that the system is operational (i.e., not undergoing repair or preventive maintenance). The optimal rate of preventive maintenance to maximize availability is considered, along with sufficient conditions for such an optimum to exist. The results obtained herein are useful to systems engineers in making critical design decisions.

Burn-in and Maintenance Policies

1994 ◽  
Vol 26 (1) ◽  
pp. 207-221 ◽  
Author(s):  
Jie Mi
Keyword(s):  
Preventive Maintenance ◽  
Minimal Repair ◽  
Maintenance And Repair ◽  
Cost Structures ◽  
Related Cost ◽  
Optimal Maintenance ◽  
Maintenance Policies ◽  
Quality Of Products ◽  
Age Replacement

Burn-in is a widely used method to improve quality of products after they have been produced. For a repairable component there are two common types of repair, complete repair and minimal repair. Preventive maintenance policies such as age replacement and block replacement are often employed in field operation. The present paper takes burn-in, maintenance and repair into consideration at the same time and considers related cost structures. The properties of the corresponding optimal burn-in times and optimal maintenance policies are discussed.

scholarly journals Optimal Maintenance Policy for a Technical System Subject to Hidden Faults and Randomly Occurring Hazards

2020 ◽  
Vol 6 (1) ◽  
pp. 396-415
Author(s):  
Jacek Malinowski
Keyword(s):  
Poisson Process ◽  
Preventive Maintenance ◽  
Cost Effective ◽  
Optimal Time ◽  
Maintenance Policy ◽  
Modeling And Analysis ◽  
Optimal Maintenance ◽  
Simple Equations ◽  
Periodic Inspections ◽  
Optimal Maintenance Policy

The paper presents a method of finding the optimal time between inspections for a system subject to degradation-related faults which make the system vulnerable to randomly occurring external hazards that may cause its damage. Since faults are assumed to be hidden, periodic inspections and repairs have to be performed in order to detect and remove them. Otherwise, leaving the faulty system unmaintained would eventually lead to a very costly damage. It is also assumed that the time to occurrence of a fault is exponentially distributed and hazardous events constitute a Poisson process. The fault rate, the intensity of the Poisson process and the probability with which a hazardous event results in the system damage are the known parameters. The author presents two main results achieved by analyzing this maintenance model. First, the criteria to be fulfilled by the system parameters in order that preventive maintenance be cost-effective are given in the form of simple inequalities. These criteria must be met so that operating the system with preventive maintenance in place be less costly than operating it until a damage occurs and replacing it thereafter. Second, fairly simple equations are obtained from which the optimal time between inspections can be found numerically by the Newton-Raphson method. The analytical derivation of both the criteria and the equations is presented in detail and is the author’s original work. To the best of his knowledge the obtained results are new in the area of maintenance modeling and analysis. For better understanding, theoretical considerations are illustrated by an example of a generic explosion prevention system.

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