{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/99171"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/99171","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Sensor-based maintenance cost estimation and residual value estimation for medical equipment recycling","abstract":"Medical equipment manufacturing companies that provide maintenance and product take-back programs have limited information about location and severity of defects, and the condition of the product before making decisions about whether to repair, recycle, remanufacture, refurbish or call-back and scrap the product. The first step is to estimate the residual value of the product. The current methods depends on the owner-claimed condition of the product and the number of accessories. However, the claimed condition is highly subjective and of high degree of variability, even of the same claimed condition and number of accessories. Such high variability in real value is considered to be caused by different working environments and customer user behavior. This thesis proposes a sensor based model to more accurately predict residual value and the actual condition of the product based on measuring the environment and working condition of the product real-time. The model provides suggestions for each individual product based on its quality level and component reliability level. A patient monitor was used as an example. The simulated result showed that the proposed method could significantly reduce the loss from variance in quality of products.","abstract_html":"Medical equipment manufacturing companies that provide maintenance and product take-back programs have limited information about location and severity of defects, and the condition of the product before making decisions about whether to repair, recycle, remanufacture, refurbish or call-back and scrap the product. The first step is to estimate the residual value of the product. The current methods depends on the owner-claimed condition of the product and the number of accessories. However, the claimed condition is highly subjective and of high degree of variability, even of the same claimed condition and number of accessories. Such high variability in real value is considered to be caused by different working environments and customer user behavior. This thesis proposes a sensor based model to more accurately predict residual value and the actual condition of the product based on measuring the environment and working condition of the product real-time. The model provides suggestions for each individual product based on its quality level and component reliability level. A patient monitor was used as an example. The simulated result showed that the proposed method could significantly reduce the loss from variance in quality of products.","abstract_has_math":false,"creators":["Liu, Xinlu"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Systems & Entrepreneurial Engr","degree_department":null,"school":null,"contributors":["Thurston, Deborah"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018-03-13T15:20:55Z","date_published":"2018-03-13T15:20:55Z","updated_at":"2026-07-22T22:24:37Z","subjects":["Sensor","Residual value estimation"],"languages":["en"],"rights":["Copyright 2017 Xinlu Liu"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/99171","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Thurston, Deborah"]},{"key":"dc:creator","label":"Author","values":["Liu, Xinlu"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2018-03-13T15:20:55Z","2020-03-14T09:15:16Z","2017-09-13","2017-12"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Systems & Entrepreneurial Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Sensor","Residual value estimation"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2017 Xinlu Liu"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/99171"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Medical equipment manufacturing companies that provide maintenance and product take-back programs have limited information about location and severity of defects, and the condition of the product before making decisions about whether to repair, recycle, remanufacture, refurbish or call-back and scrap the product. The first step is to estimate the residual value of the product. The current methods depends on the owner-claimed condition of the product and the number of accessories. However, the claimed condition is highly subjective and of high degree of variability, even of the same claimed condition and number of accessories. Such high variability in real value is considered to be caused by different working environments and customer user behavior. This thesis proposes a sensor based model to more accurately predict residual value and the actual condition of the product based on measuring the environment and working condition of the product real-time. The model provides suggestions for each individual product based on its quality level and component reliability level. A patient monitor was used as an example. The simulated result showed that the proposed method could significantly reduce the loss from variance in quality of products.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2019-12-01","The student, Xinlu Liu, accepted the attached license on 2017-08-28 at 11:29.","The student, Xinlu Liu, submitted this Thesis for approval on 2017-08-28 at 11:36.","This Thesis was approved for publication on 2017-09-13 at 14:12.","DSpace SAF Submission Ingestion Package generated from Vireo submission #11618 on 2018-03-13 at 09:54:43","Made available in DSpace on 2018-03-13T15:20:55Z (GMT). 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The first step is to estimate the residual value of the product. The current methods depends on the owner-claimed condition of the product and the number of accessories. However, the claimed condition is highly subjective and of high degree of variability, even of the same claimed condition and number of accessories. Such high variability in real value is considered to be caused by different working environments and customer user behavior. This thesis proposes a sensor based model to more accurately predict residual value and the actual condition of the product based on measuring the environment and working condition of the product real-time. The model provides suggestions for each individual product based on its quality level and component reliability level. A patient monitor was used as an example. The simulated result showed that the proposed method could significantly reduce the loss from variance in quality of products.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2019-12-01","The student, Xinlu Liu, accepted the attached license on 2017-08-28 at 11:29.","The student, Xinlu Liu, submitted this Thesis for approval on 2017-08-28 at 11:36.","This Thesis was approved for publication on 2017-09-13 at 14:12.","DSpace SAF Submission Ingestion Package generated from Vireo submission #11618 on 2018-03-13 at 09:54:43","Made available in DSpace on 2018-03-13T15:20:55Z (GMT). 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