{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T19:13:09Z","timestamp":1775589189038,"version":"3.50.1"},"reference-count":22,"publisher":"IEEE","license":[{"start":{"date-parts":[[2019,12,1]],"date-time":"2019-12-01T00:00:00Z","timestamp":1575158400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2019,12,1]],"date-time":"2019-12-01T00:00:00Z","timestamp":1575158400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2019,12,1]],"date-time":"2019-12-01T00:00:00Z","timestamp":1575158400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,12]]},"DOI":"10.1109\/bigdata47090.2019.9006213","type":"proceedings-article","created":{"date-parts":[[2020,2,25]],"date-time":"2020-02-25T01:05:34Z","timestamp":1582592734000},"page":"4366-4374","source":"Crossref","is-referenced-by-count":18,"title":["Prescriptive Equipment Maintenance: A Framework"],"prefix":"10.1109","author":[{"given":"Suresh","family":"Choubey","sequence":"first","affiliation":[]},{"given":"Ryan","family":"Benton","sequence":"additional","affiliation":[]},{"given":"Tom","family":"Johnsten","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref10","doi-asserted-by":"crossref","first-page":"13692","DOI":"10.1016\/j.ifacol.2017.08.2536","article-title":"Sustainable maintenance: a periodic preventive maintenance model with sustainable spare parts management","volume":"50","author":"franciosi","year":"2017","journal-title":"IFAC-PapersOnLine"},{"key":"ref11","doi-asserted-by":"crossref","first-page":"1127","DOI":"10.1016\/j.promfg.2017.07.234","article-title":"Mining shop-floor data for preventive maintenance management: integrating probabilistic and predictive models","author":"ruschel","year":"2017","journal-title":"Procedia Manufacturing"},{"key":"ref12","first-page":"1","article-title":"Prescriptive maintenance of CPPS by integrating multimodal data with dynamic bayesian networks","author":"ansari","year":"2017","journal-title":"International Conference on Machine Learning for Cyber Physical Systems"},{"key":"ref13","doi-asserted-by":"crossref","first-page":"31","DOI":"10.3182\/20090603-3-RU-2001.0578","article-title":"SmartFactory -- a vision Becomes Reality","volume":"42","author":"zuehlke","year":"2009","journal-title":"IFAC Proceedings Volumes"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1016\/j.cirp.2017.04.007"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijinfomgt.2019.04.003"},{"key":"ref16","first-page":"313","article-title":"A holistic approach for anticipative maintenance planning supported by a dynamic calculation of wear reserve","volume":"1","author":"glawar","year":"2016","journal-title":"Engineering Maintenance"},{"key":"ref17","doi-asserted-by":"crossref","first-page":"1153","DOI":"10.1016\/j.promfg.2017.07.239","article-title":"Data mining and machine learning for condition-based maintenance","volume":"11","author":"accorsi","year":"0","journal-title":"Procedia Manufacturing"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejor.2017.04.019"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2017.08.025"},{"key":"ref4","author":"han","year":"2001","journal-title":"Data Mining Concepts and Techniques Prentice Hall"},{"key":"ref3","year":"2019"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.3233\/AIC-1994-7104"},{"key":"ref5","year":"0"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1007\/s10845-015-1179-5"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.compind.2018.04.019"},{"key":"ref2","year":"2019"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1080\/0951192X.2019.1571236"},{"key":"ref9","author":"kelleher","year":"2015","journal-title":"Fundamentals of machine learning for predictive data analytics algorithms worked examples and case studies MIT Press"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijinfomgt.2019.01.020"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1972.tb00899.x"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejor.2018.06.035"}],"event":{"name":"2019 IEEE International Conference on Big Data (Big Data)","location":"Los Angeles, CA, USA","start":{"date-parts":[[2019,12,9]]},"end":{"date-parts":[[2019,12,12]]}},"container-title":["2019 IEEE International Conference on Big Data (Big Data)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/8986695\/9005444\/09006213.pdf?arnumber=9006213","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,17]],"date-time":"2022-07-17T17:48:26Z","timestamp":1658080106000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9006213\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,12]]},"references-count":22,"URL":"https:\/\/doi.org\/10.1109\/bigdata47090.2019.9006213","relation":{},"subject":[],"published":{"date-parts":[[2019,12]]}}}