A Situational Based Reliability Indices Estimation of ULT Freezer using Preventive Maintenance under Fuzzy Environment

Reliability of high demand machines is quite necessary and it can be maintained through proper and timely maintenance, Ultra-low temperature (ULT) freezer is one of those kinds of machines which are in high demand during covid-19 pandemic for the storage of vaccine. The rapid production of vaccines...

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Main Authors: Pooja Dhiman, Amit Kumar
Format: Article
Language:English
Published: Ram Arti Publishers 2023-06-01
Series:International Journal of Mathematical, Engineering and Management Sciences
Subjects:
Online Access:https://www.ijmems.in/cms/storage/app/public/uploads/volumes/27-IJMEMS-22-0711-8-3-477-503-2023.pdf
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author Pooja Dhiman
Amit Kumar
author_facet Pooja Dhiman
Amit Kumar
author_sort Pooja Dhiman
collection DOAJ
description Reliability of high demand machines is quite necessary and it can be maintained through proper and timely maintenance, Ultra-low temperature (ULT) freezer is one of those kinds of machines which are in high demand during covid-19 pandemic for the storage of vaccine. The rapid production of vaccines for the prevention of coronavirus disease 2019 (COVID-19) is a worldwide requirement. Now the next challenge is to store the vaccine in a ULT freezer. It’s become really a big problem to store the vaccine which creates the demand of ULT freezer. The present paper investigates a situational based performance of the ULT freezer with the aim to predict the impact of different component failures as well as human errors on the final performance of the same. For the study, it is not possible to extract the parameters (failure rate and repair time) of the components that never failed before. Thus, to overcome this difficulty, here authors use the possibility theory. Authors present the available data in Right triangular fuzzy number with some tolerance as suggested by system analyst. The lambda-tau methodology and arithmetic operations on right triangular generalized fuzzy numbers (RTrFN) are used to find the various performance parameters namely MTTF, MTTR, MTBF, reliability, availability, maintainability (RAM) and ENOF, under fuzzy environment. The proposed model has been studied using possibility theory under working conditions, preventive maintenance as well as under the rest of conditions. This study reveals the most and least critical component of the ULT freezer which helps maintenance department to plan the maintenance strategy accordingly.
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spelling doaj.art-8de888cdba0648fdb2238ba3a4701f052023-04-09T10:47:27ZengRam Arti PublishersInternational Journal of Mathematical, Engineering and Management Sciences2455-77492023-06-0183477503https://doi.org/10.33889/IJMEMS.2023.8.3.027A Situational Based Reliability Indices Estimation of ULT Freezer using Preventive Maintenance under Fuzzy EnvironmentPooja Dhiman0Amit Kumar1Department of Mathematics, Chitkara University, Punjab, India.Department of Applied Sciences, Symbiosis Institute of Technology, Symbiosis International (Deemed University) (SIU), Lavale, Pune, Maharashtra, India.Reliability of high demand machines is quite necessary and it can be maintained through proper and timely maintenance, Ultra-low temperature (ULT) freezer is one of those kinds of machines which are in high demand during covid-19 pandemic for the storage of vaccine. The rapid production of vaccines for the prevention of coronavirus disease 2019 (COVID-19) is a worldwide requirement. Now the next challenge is to store the vaccine in a ULT freezer. It’s become really a big problem to store the vaccine which creates the demand of ULT freezer. The present paper investigates a situational based performance of the ULT freezer with the aim to predict the impact of different component failures as well as human errors on the final performance of the same. For the study, it is not possible to extract the parameters (failure rate and repair time) of the components that never failed before. Thus, to overcome this difficulty, here authors use the possibility theory. Authors present the available data in Right triangular fuzzy number with some tolerance as suggested by system analyst. The lambda-tau methodology and arithmetic operations on right triangular generalized fuzzy numbers (RTrFN) are used to find the various performance parameters namely MTTF, MTTR, MTBF, reliability, availability, maintainability (RAM) and ENOF, under fuzzy environment. The proposed model has been studied using possibility theory under working conditions, preventive maintenance as well as under the rest of conditions. This study reveals the most and least critical component of the ULT freezer which helps maintenance department to plan the maintenance strategy accordingly.https://www.ijmems.in/cms/storage/app/public/uploads/volumes/27-IJMEMS-22-0711-8-3-477-503-2023.pdfult freezeruncertaintyreliabilityconfidence levelfuzzy number
spellingShingle Pooja Dhiman
Amit Kumar
A Situational Based Reliability Indices Estimation of ULT Freezer using Preventive Maintenance under Fuzzy Environment
International Journal of Mathematical, Engineering and Management Sciences
ult freezer
uncertainty
reliability
confidence level
fuzzy number
title A Situational Based Reliability Indices Estimation of ULT Freezer using Preventive Maintenance under Fuzzy Environment
title_full A Situational Based Reliability Indices Estimation of ULT Freezer using Preventive Maintenance under Fuzzy Environment
title_fullStr A Situational Based Reliability Indices Estimation of ULT Freezer using Preventive Maintenance under Fuzzy Environment
title_full_unstemmed A Situational Based Reliability Indices Estimation of ULT Freezer using Preventive Maintenance under Fuzzy Environment
title_short A Situational Based Reliability Indices Estimation of ULT Freezer using Preventive Maintenance under Fuzzy Environment
title_sort situational based reliability indices estimation of ult freezer using preventive maintenance under fuzzy environment
topic ult freezer
uncertainty
reliability
confidence level
fuzzy number
url https://www.ijmems.in/cms/storage/app/public/uploads/volumes/27-IJMEMS-22-0711-8-3-477-503-2023.pdf
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