A Knowledge-Based Framework for Cost Implication Modeling of Mechanically Repairable Systems with Imperfect Preventive Maintenance and Replacement Schedule

A knowledge-based framework that exploits fuzzy logic to generate precise cost implication decisions from an optimal maintenance and replacement schedule is proposed. Using data from a locally fabricated 8HP-PML Gold engine cassava grinding machine whose failure distribution follows the Weibull dist...

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Main Authors: Nse Udoh, Moses Ekpenyong
Format: Article
Language:English
Published: Tamkang University Press 2022-05-01
Series:Journal of Applied Science and Engineering
Subjects:
Online Access:http://jase.tku.edu.tw/articles/jase-202302-26-2-0008
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author Nse Udoh
Moses Ekpenyong
author_facet Nse Udoh
Moses Ekpenyong
author_sort Nse Udoh
collection DOAJ
description A knowledge-based framework that exploits fuzzy logic to generate precise cost implication decisions from an optimal maintenance and replacement schedule is proposed. Using data from a locally fabricated 8HP-PML Gold engine cassava grinding machine whose failure distribution follows the Weibull distribution function with shape and scale parameters α=1.30 and β =1386, respectively; and cost input parameters namely, the cost of preventive maintenance (Cp), cost of replacement maintenance (Cr), and cost of minimal repair (Cm), an analytical model was constructed to generate the corresponding optimal cost ratios (Cr⁄Cp and Cm⁄Cp ), useful for deriving the required universe of discourse and membership functions for the respective linguistic variables or cost parameters ranges. Extensive simulation using MATLAB 2017a revealed three types of system performance demonstrating the effects of costs interaction on varying costs implication decisions. Results of simulation indicate that the machine functions optimally at all low costs (i.e., when Cp, Cr, and Cm are ‘low’) and maintains delayed replacement frequencies but the machine becomes expensive to maintain when Cp, and Cm increases above acceptable thresholds (i.e., are either ‘high’ or ‘v.high’). The scientific implication is that the proposed system efficiently models interaction between input parameters and can effectively guide operators/designers’ decisions on the choice to weigh varying cost implication decisions of PM and replacement schedules for mechanically repairable systems whose failure rate may be characterized by other failure distribution functions.
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spelling doaj.art-4e8f4ec411084b478238c7d3e43be5d32022-12-22T00:38:19ZengTamkang University PressJournal of Applied Science and Engineering2708-99672708-99752022-05-01261221234 10.6180/jase.202302_26(2).0008A Knowledge-Based Framework for Cost Implication Modeling of Mechanically Repairable Systems with Imperfect Preventive Maintenance and Replacement ScheduleNse Udoh0Moses Ekpenyong1Department of Statistics, University of Uyo, NigeriaDepartment of Computer Science, University of Uyo, NigeriaA knowledge-based framework that exploits fuzzy logic to generate precise cost implication decisions from an optimal maintenance and replacement schedule is proposed. Using data from a locally fabricated 8HP-PML Gold engine cassava grinding machine whose failure distribution follows the Weibull distribution function with shape and scale parameters α=1.30 and β =1386, respectively; and cost input parameters namely, the cost of preventive maintenance (Cp), cost of replacement maintenance (Cr), and cost of minimal repair (Cm), an analytical model was constructed to generate the corresponding optimal cost ratios (Cr⁄Cp and Cm⁄Cp ), useful for deriving the required universe of discourse and membership functions for the respective linguistic variables or cost parameters ranges. Extensive simulation using MATLAB 2017a revealed three types of system performance demonstrating the effects of costs interaction on varying costs implication decisions. Results of simulation indicate that the machine functions optimally at all low costs (i.e., when Cp, Cr, and Cm are ‘low’) and maintains delayed replacement frequencies but the machine becomes expensive to maintain when Cp, and Cm increases above acceptable thresholds (i.e., are either ‘high’ or ‘v.high’). The scientific implication is that the proposed system efficiently models interaction between input parameters and can effectively guide operators/designers’ decisions on the choice to weigh varying cost implication decisions of PM and replacement schedules for mechanically repairable systems whose failure rate may be characterized by other failure distribution functions.http://jase.tku.edu.tw/articles/jase-202302-26-2-0008failure distributionfuzzy logicpreventive maintenancepredictive maintenancereplacement scheduleweibull distribution function
spellingShingle Nse Udoh
Moses Ekpenyong
A Knowledge-Based Framework for Cost Implication Modeling of Mechanically Repairable Systems with Imperfect Preventive Maintenance and Replacement Schedule
Journal of Applied Science and Engineering
failure distribution
fuzzy logic
preventive maintenance
predictive maintenance
replacement schedule
weibull distribution function
title A Knowledge-Based Framework for Cost Implication Modeling of Mechanically Repairable Systems with Imperfect Preventive Maintenance and Replacement Schedule
title_full A Knowledge-Based Framework for Cost Implication Modeling of Mechanically Repairable Systems with Imperfect Preventive Maintenance and Replacement Schedule
title_fullStr A Knowledge-Based Framework for Cost Implication Modeling of Mechanically Repairable Systems with Imperfect Preventive Maintenance and Replacement Schedule
title_full_unstemmed A Knowledge-Based Framework for Cost Implication Modeling of Mechanically Repairable Systems with Imperfect Preventive Maintenance and Replacement Schedule
title_short A Knowledge-Based Framework for Cost Implication Modeling of Mechanically Repairable Systems with Imperfect Preventive Maintenance and Replacement Schedule
title_sort knowledge based framework for cost implication modeling of mechanically repairable systems with imperfect preventive maintenance and replacement schedule
topic failure distribution
fuzzy logic
preventive maintenance
predictive maintenance
replacement schedule
weibull distribution function
url http://jase.tku.edu.tw/articles/jase-202302-26-2-0008
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