{"id":"https://openalex.org/W2513688333","doi":"https://doi.org/10.1109/icphm.2016.7542876","title":"Developing machine learning-based models to estimate time to failure for PHM","display_name":"Developing machine learning-based models to estimate time to failure for PHM","publication_year":2016,"publication_date":"2016-06-01","ids":{"openalex":"https://openalex.org/W2513688333","doi":"https://doi.org/10.1109/icphm.2016.7542876","mag":"2513688333"},"language":"en","primary_location":{"id":"doi:10.1109/icphm.2016.7542876","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icphm.2016.7542876","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Prognostics and Health Management (ICPHM)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5103207845","display_name":"Yang Chun-sheng","orcid":"https://orcid.org/0000-0002-9404-2003"},"institutions":[{"id":"https://openalex.org/I4210159778","display_name":"National Research Council Canada","ror":"https://ror.org/04mte1k06","country_code":"CA","type":"government","lineage":["https://openalex.org/I4210159778"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Chunsheng Yang","raw_affiliation_strings":["National Research Council Canada, Ottawa, Ontario, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Research Council Canada, Ottawa, Ontario, Canada","institution_ids":["https://openalex.org/I4210159778"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015129790","display_name":"Takayuki It\u014d","orcid":"https://orcid.org/0000-0001-5093-3886"},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Takayuki Ito","raw_affiliation_strings":["State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China","institution_ids":["https://openalex.org/I881766915"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111673780","display_name":"Yu-Bin Yang","orcid":"https://orcid.org/0000-0002-3764-1114"},"institutions":[{"id":"https://openalex.org/I197274945","display_name":"Nagoya Institute of Technology","ror":"https://ror.org/055yf1005","country_code":"JP","type":"education","lineage":["https://openalex.org/I197274945"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yubin Yang","raw_affiliation_strings":["Nagoya Institute of Technology, Nagoya, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nagoya Institute of Technology, Nagoya, Japan","institution_ids":["https://openalex.org/I197274945"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100454179","display_name":"Jie Liu","orcid":"https://orcid.org/0000-0003-0895-7598"},"institutions":[{"id":"https://openalex.org/I67031392","display_name":"Carleton University","ror":"https://ror.org/02qtvee93","country_code":"CA","type":"education","lineage":["https://openalex.org/I67031392"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Jie Liu","raw_affiliation_strings":["Carleton University, Ottawa, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Carleton University, Ottawa, Canada","institution_ids":["https://openalex.org/I67031392"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.9875,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.90557621,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10780","display_name":"Reliability and Maintenance Optimization","score":0.9980999827384949,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10780","display_name":"Reliability and Maintenance Optimization","score":0.9980999827384949,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.9950000047683716,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12423","display_name":"Software Reliability and Analysis Research","score":0.9829999804496765,"subfield":{"id":"https://openalex.org/subfields/1712","display_name":"Software"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/prognostics","display_name":"Prognostics","score":0.9951213598251343},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6465210318565369},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5967262387275696},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.5396767258644104},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5355783104896545},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.4339025318622589},{"id":"https://openalex.org/keywords/condition-monitoring","display_name":"Condition monitoring","score":0.41520261764526367},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.38397419452667236},{"id":"https://openalex.org/keywords/reliability-engineering","display_name":"Reliability engineering","score":0.34032803773880005},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.31430184841156006}],"concepts":[{"id":"https://openalex.org/C129364497","wikidata":"https://www.wikidata.org/wiki/Q3042561","display_name":"Prognostics","level":2,"score":0.9951213598251343},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6465210318565369},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5967262387275696},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.5396767258644104},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5355783104896545},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4339025318622589},{"id":"https://openalex.org/C2775846686","wikidata":"https://www.wikidata.org/wiki/Q643012","display_name":"Condition monitoring","level":2,"score":0.41520261764526367},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.38397419452667236},{"id":"https://openalex.org/C200601418","wikidata":"https://www.wikidata.org/wiki/Q2193887","display_name":"Reliability engineering","level":1,"score":0.34032803773880005},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.31430184841156006},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/icphm.2016.7542876","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icphm.2016.7542876","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Prognostics and Health Management (ICPHM)","raw_type":"proceedings-article"},{"id":"pmh:oai:cisti-icist.nrc-cnrc.ca:cistinparc:23000672","is_oa":false,"landing_page_url":"https://nrc-publications.canada.ca/eng/view/object/?id=73ddf38f-568a-4745-87f4-d7094aeb8ef4","pdf_url":null,"source":{"id":"https://openalex.org/S7407055245","display_name":"NPARC","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W1969020285","https://openalex.org/W2036541533","https://openalex.org/W2042311265","https://openalex.org/W2055873761","https://openalex.org/W2073100474","https://openalex.org/W2094305437","https://openalex.org/W2099349620","https://openalex.org/W2114106396","https://openalex.org/W2119003537","https://openalex.org/W2142933466","https://openalex.org/W2155832827","https://openalex.org/W2164917791","https://openalex.org/W3105441930","https://openalex.org/W3137727436","https://openalex.org/W4233272741","https://openalex.org/W6792188030"],"related_works":["https://openalex.org/W2557573737","https://openalex.org/W2383842997","https://openalex.org/W2143585755","https://openalex.org/W2908973203","https://openalex.org/W2801712269","https://openalex.org/W1872896676","https://openalex.org/W2156691445","https://openalex.org/W2045186954","https://openalex.org/W3000986292","https://openalex.org/W1502469213"],"abstract_inverted_index":{"The":[0,122,156],"core":[1],"of":[2,34,78,110,152],"PHM":[3],"(Prognostic":[4],"and":[5,64,85,99,172],"Health":[6],"Monitoring)":[7],"technology":[8,89],"is":[9,12,169],"prognostics":[10],"which":[11],"able":[13],"to":[14,17,49,90,175,179],"estimate":[15,180],"time":[16,136,142],"failure":[18],"(TTF)":[19],"for":[20,37,54,82,114,182],"the":[21,27,32,59,68,76,87,126,135,150,153,159,164],"monitored":[22],"components":[23],"or":[24],"systems":[25],"using":[26,58],"built-in":[28],"predictive":[29,35,177],"models.":[30],"However":[31],"development":[33,77],"models":[36,53,81,113,178],"TTF":[38,55,84,127,181],"estimation":[39,56,128],"remains":[40],"a":[41],"challenge.":[42],"To":[43],"address":[44],"this":[45,104],"issue,":[46],"we":[47,71,106],"proposed":[48],"develop":[50,176],"machine":[51,62,79,111,165],"learning-based":[52,80,112,166],"by":[57,133],"techniques":[60],"from":[61,158],"learning":[63],"data":[65],"mining.":[66],"In":[67,103],"past":[69],"decade,":[70],"have":[72],"been":[73],"working":[74],"on":[75],"estimating":[83,115],"applied":[86],"developed":[88,154],"various":[91],"real-world":[92],"applications":[93],"such":[94],"as":[95],"train":[96],"wheel":[97],"prognostics,":[98,148],"aircraft":[100],"engine":[101],"prognostics.":[102],"paper,":[105],"report":[107],"two":[108],"kinds":[109],"TTF,":[116],"including":[117],"multistage":[118,123],"classification,":[119],"on-demand":[120],"regression.":[121],"classification":[124,132],"improves":[125],"over":[129],"one":[130],"stage":[131],"dividing":[134],"window":[137],"into":[138],"more":[139],"small":[140],"narrow":[141],"windows.":[143],"A":[144],"case":[145,160],"study,":[146],"APU":[147],"demonstrates":[149],"usefulness":[151],"methods.":[155],"results":[157],"study":[161],"show":[162],"that":[163],"modeling":[167],"method":[168],"an":[170],"effective":[171],"feasible":[173],"way":[174],"PHM.":[183]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":2}],"updated_date":"2026-08-21T09:56:20.448147","created_date":"2025-10-10T00:00:00"}
