Early stage data-based probabilistic wear life prediction and maintenance interval optimization of driving wheels
•Establishing probabilistic wear model and verifying with experiment.•Creating a recursive model to predict wear life using early stage data.•The maintenance ad cost strategies considering the different wheel combinations.•Building multi-objective maintenance interval optimization method under relia...
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Veröffentlicht in: | Reliability engineering & system safety 2020-05, Vol.197, p.106791-13, Article 106791 |
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creator | Chang, Mingu Lee, Jongsoo |
description | •Establishing probabilistic wear model and verifying with experiment.•Creating a recursive model to predict wear life using early stage data.•The maintenance ad cost strategies considering the different wheel combinations.•Building multi-objective maintenance interval optimization method under reliability.
This study presents an early stage data-based maintenance strategy of driving wheels that have different life distributions depending upon their location. Wear was predicted under the condition that the shape of the contact surface changes over time by an original method of back calculating degradation over time through the establishment of a basic wear model and a recursive function for wear progression. An accurate wear model was established and verified by an experiment. The variation in the profile of a wear-induced wheel was applied to the wear model. Furthermore, the model was combined with a recursive function and used to obtain the time-series degradation data. Subsequently, the factors which have a major influence on wheel production were analyzed, and a meta-model was configured using the response surface method. The degradation function and parameter distribution were estimated using uncertainty propagation, and the wear life distribution was derived using Bayesian inference and Markov chain Monte Carlo method. The reliability of driving wheels was obtained, and the maintenance interval was optimized under each maintenance conditions. Based on this novel method, the early stage data-based maintenance strategy was achieved, and the result of the wear life prediction was validated using the probability distribution analysis. |
doi_str_mv | 10.1016/j.ress.2020.106791 |
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This study presents an early stage data-based maintenance strategy of driving wheels that have different life distributions depending upon their location. Wear was predicted under the condition that the shape of the contact surface changes over time by an original method of back calculating degradation over time through the establishment of a basic wear model and a recursive function for wear progression. An accurate wear model was established and verified by an experiment. The variation in the profile of a wear-induced wheel was applied to the wear model. Furthermore, the model was combined with a recursive function and used to obtain the time-series degradation data. Subsequently, the factors which have a major influence on wheel production were analyzed, and a meta-model was configured using the response surface method. The degradation function and parameter distribution were estimated using uncertainty propagation, and the wear life distribution was derived using Bayesian inference and Markov chain Monte Carlo method. The reliability of driving wheels was obtained, and the maintenance interval was optimized under each maintenance conditions. Based on this novel method, the early stage data-based maintenance strategy was achieved, and the result of the wear life prediction was validated using the probability distribution analysis.</description><identifier>ISSN: 0951-8320</identifier><identifier>EISSN: 1879-0836</identifier><identifier>DOI: 10.1016/j.ress.2020.106791</identifier><language>eng</language><publisher>Barking: Elsevier Ltd</publisher><subject>Bayesian analysis ; Bayesian inference ; Computer simulation ; Degradation ; Driving wheels ; Life prediction ; Maintenance ; Markov chain Monte Carlo ; Markov chains ; Monte Carlo simulation ; Optimization ; Parameter estimation ; Parameter uncertainty ; Probability distribution ; Recursive function ; Recursive functions ; Reliability engineering ; Reliability maintenance interval optimization ; Response surface methodology ; Statistical analysis ; Statistical inference ; Wear ; Wear life prediction ; Wheels</subject><ispartof>Reliability engineering & system safety, 2020-05, Vol.197, p.106791-13, Article 106791</ispartof><rights>2020 Elsevier Ltd</rights><rights>Copyright Elsevier BV May 2020</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c328t-1df5b5ec1d0bea963891b488e076b9775140c460002ac8b089ed20a603539f183</citedby><cites>FETCH-LOGICAL-c328t-1df5b5ec1d0bea963891b488e076b9775140c460002ac8b089ed20a603539f183</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.ress.2020.106791$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3548,27923,27924,45994</link.rule.ids></links><search><creatorcontrib>Chang, Mingu</creatorcontrib><creatorcontrib>Lee, Jongsoo</creatorcontrib><title>Early stage data-based probabilistic wear life prediction and maintenance interval optimization of driving wheels</title><title>Reliability engineering & system safety</title><description>•Establishing probabilistic wear model and verifying with experiment.•Creating a recursive model to predict wear life using early stage data.•The maintenance ad cost strategies considering the different wheel combinations.•Building multi-objective maintenance interval optimization method under reliability.
This study presents an early stage data-based maintenance strategy of driving wheels that have different life distributions depending upon their location. Wear was predicted under the condition that the shape of the contact surface changes over time by an original method of back calculating degradation over time through the establishment of a basic wear model and a recursive function for wear progression. An accurate wear model was established and verified by an experiment. The variation in the profile of a wear-induced wheel was applied to the wear model. Furthermore, the model was combined with a recursive function and used to obtain the time-series degradation data. Subsequently, the factors which have a major influence on wheel production were analyzed, and a meta-model was configured using the response surface method. The degradation function and parameter distribution were estimated using uncertainty propagation, and the wear life distribution was derived using Bayesian inference and Markov chain Monte Carlo method. The reliability of driving wheels was obtained, and the maintenance interval was optimized under each maintenance conditions. Based on this novel method, the early stage data-based maintenance strategy was achieved, and the result of the wear life prediction was validated using the probability distribution analysis.</description><subject>Bayesian analysis</subject><subject>Bayesian inference</subject><subject>Computer simulation</subject><subject>Degradation</subject><subject>Driving wheels</subject><subject>Life prediction</subject><subject>Maintenance</subject><subject>Markov chain Monte Carlo</subject><subject>Markov chains</subject><subject>Monte Carlo simulation</subject><subject>Optimization</subject><subject>Parameter estimation</subject><subject>Parameter uncertainty</subject><subject>Probability distribution</subject><subject>Recursive function</subject><subject>Recursive functions</subject><subject>Reliability engineering</subject><subject>Reliability maintenance interval optimization</subject><subject>Response surface methodology</subject><subject>Statistical analysis</subject><subject>Statistical inference</subject><subject>Wear</subject><subject>Wear life prediction</subject><subject>Wheels</subject><issn>0951-8320</issn><issn>1879-0836</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><recordid>eNp9kEtPwzAQhC0EEuXxBzhZ4pxi52lLXFBVHlIlLnC2NvamOEqT1nZblV-PQzhz2tVoZlfzEXLH2ZwzXj60c4fez1OWjkJZSX5GZlxUMmEiK8_JjMmCJyJL2SW58r5ljOWyqGZktwTXnagPsEZqIEBSg0dDt26oobad9cFqekRwtLMNRh2N1cEOPYXe0A3YPmAPvUY6bu4AHR22wW7sN_y6hoYaZw-2X9PjF2Lnb8hFA53H2795TT6flx-L12T1_vK2eFolOktFSLhpirpAzQ2rEWSZCcnrXAhkVVnLqip4znRexiIpaFEzIdGkDEqWFZlsuMiuyf10N1bZ7dEH1Q5718eXKs3zSsT-mYyudHJpN3jvsFFbZzfgToozNaJVrRrRqhGtmtDG0OMUinXwYNEpry1GBsY61EGZwf4X_wE3tIN_</recordid><startdate>202005</startdate><enddate>202005</enddate><creator>Chang, Mingu</creator><creator>Lee, Jongsoo</creator><general>Elsevier Ltd</general><general>Elsevier BV</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7ST</scope><scope>7TB</scope><scope>8FD</scope><scope>C1K</scope><scope>FR3</scope><scope>SOI</scope></search><sort><creationdate>202005</creationdate><title>Early stage data-based probabilistic wear life prediction and maintenance interval optimization of driving wheels</title><author>Chang, Mingu ; Lee, Jongsoo</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c328t-1df5b5ec1d0bea963891b488e076b9775140c460002ac8b089ed20a603539f183</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Bayesian analysis</topic><topic>Bayesian inference</topic><topic>Computer simulation</topic><topic>Degradation</topic><topic>Driving wheels</topic><topic>Life prediction</topic><topic>Maintenance</topic><topic>Markov chain Monte Carlo</topic><topic>Markov chains</topic><topic>Monte Carlo simulation</topic><topic>Optimization</topic><topic>Parameter estimation</topic><topic>Parameter uncertainty</topic><topic>Probability distribution</topic><topic>Recursive function</topic><topic>Recursive functions</topic><topic>Reliability engineering</topic><topic>Reliability maintenance interval optimization</topic><topic>Response surface methodology</topic><topic>Statistical analysis</topic><topic>Statistical inference</topic><topic>Wear</topic><topic>Wear life prediction</topic><topic>Wheels</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Chang, Mingu</creatorcontrib><creatorcontrib>Lee, Jongsoo</creatorcontrib><collection>CrossRef</collection><collection>Environment Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Engineering Research Database</collection><collection>Environment Abstracts</collection><jtitle>Reliability engineering & system safety</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Chang, Mingu</au><au>Lee, Jongsoo</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Early stage data-based probabilistic wear life prediction and maintenance interval optimization of driving wheels</atitle><jtitle>Reliability engineering & system safety</jtitle><date>2020-05</date><risdate>2020</risdate><volume>197</volume><spage>106791</spage><epage>13</epage><pages>106791-13</pages><artnum>106791</artnum><issn>0951-8320</issn><eissn>1879-0836</eissn><abstract>•Establishing probabilistic wear model and verifying with experiment.•Creating a recursive model to predict wear life using early stage data.•The maintenance ad cost strategies considering the different wheel combinations.•Building multi-objective maintenance interval optimization method under reliability.
This study presents an early stage data-based maintenance strategy of driving wheels that have different life distributions depending upon their location. Wear was predicted under the condition that the shape of the contact surface changes over time by an original method of back calculating degradation over time through the establishment of a basic wear model and a recursive function for wear progression. An accurate wear model was established and verified by an experiment. The variation in the profile of a wear-induced wheel was applied to the wear model. Furthermore, the model was combined with a recursive function and used to obtain the time-series degradation data. Subsequently, the factors which have a major influence on wheel production were analyzed, and a meta-model was configured using the response surface method. The degradation function and parameter distribution were estimated using uncertainty propagation, and the wear life distribution was derived using Bayesian inference and Markov chain Monte Carlo method. The reliability of driving wheels was obtained, and the maintenance interval was optimized under each maintenance conditions. Based on this novel method, the early stage data-based maintenance strategy was achieved, and the result of the wear life prediction was validated using the probability distribution analysis.</abstract><cop>Barking</cop><pub>Elsevier Ltd</pub><doi>10.1016/j.ress.2020.106791</doi><tpages>13</tpages></addata></record> |
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subjects | Bayesian analysis Bayesian inference Computer simulation Degradation Driving wheels Life prediction Maintenance Markov chain Monte Carlo Markov chains Monte Carlo simulation Optimization Parameter estimation Parameter uncertainty Probability distribution Recursive function Recursive functions Reliability engineering Reliability maintenance interval optimization Response surface methodology Statistical analysis Statistical inference Wear Wear life prediction Wheels |
title | Early stage data-based probabilistic wear life prediction and maintenance interval optimization of driving wheels |
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