{"id":"https://openalex.org/W2153719785","doi":"https://doi.org/10.1109/fuzzy.2005.1452412","title":"Predicting the Best Units within a Fleet: Prognostic Capabilities Enabled by Peer Learning, Fuzzy Similarity, and Evolutionary Design Process","display_name":"Predicting the Best Units within a Fleet: Prognostic Capabilities Enabled by Peer Learning, Fuzzy Similarity, and Evolutionary Design Process","publication_year":2005,"publication_date":"2005-07-28","ids":{"openalex":"https://openalex.org/W2153719785","doi":"https://doi.org/10.1109/fuzzy.2005.1452412","mag":"2153719785"},"language":"en","primary_location":{"id":"doi:10.1109/fuzzy.2005.1452412","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fuzzy.2005.1452412","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The 14th IEEE International Conference on Fuzzy Systems, 2005. FUZZ '05.","raw_type":"proceedings-article"},"type":"article","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/A5044940414","display_name":"Piero P. Bonissone","orcid":"https://orcid.org/0000-0001-7051-2501"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"P.P. Bonissone","raw_affiliation_strings":["Niskayuna, NY, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Niskayuna, NY, USA","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5112000226","display_name":"Anil Varma","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"A. Varma","raw_affiliation_strings":["Niskayuna, NY, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Niskayuna, NY, USA","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.3783,"has_fulltext":false,"cited_by_count":23,"citation_normalized_percentile":{"value":0.8586466,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"312","last_page":"318"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10820","display_name":"Fuzzy Logic and Control Systems","score":0.983299970626831,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10820","display_name":"Fuzzy Logic and Control Systems","score":0.983299970626831,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10100","display_name":"Metaheuristic Optimization Algorithms Research","score":0.9711999893188477,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.9624000191688538,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.7511389851570129},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6218159198760986},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5481496453285217},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5164011120796204},{"id":"https://openalex.org/keywords/fuzzy-logic","display_name":"Fuzzy logic","score":0.5148386359214783},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.5084882974624634},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.4874565601348877},{"id":"https://openalex.org/keywords/relevance","display_name":"Relevance (law)","score":0.48606494069099426},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.483294278383255},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4757256805896759},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.4664228558540344},{"id":"https://openalex.org/keywords/evolutionary-algorithm","display_name":"Evolutionary algorithm","score":0.4583944082260132},{"id":"https://openalex.org/keywords/population","display_name":"Population","score":0.44521528482437134}],"concepts":[{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.7511389851570129},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6218159198760986},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5481496453285217},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5164011120796204},{"id":"https://openalex.org/C58166","wikidata":"https://www.wikidata.org/wiki/Q224821","display_name":"Fuzzy logic","level":2,"score":0.5148386359214783},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.5084882974624634},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.4874565601348877},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.48606494069099426},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.483294278383255},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4757256805896759},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.4664228558540344},{"id":"https://openalex.org/C159149176","wikidata":"https://www.wikidata.org/wiki/Q14489129","display_name":"Evolutionary algorithm","level":2,"score":0.4583944082260132},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.44521528482437134},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C149923435","wikidata":"https://www.wikidata.org/wiki/Q37732","display_name":"Demography","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/fuzzy.2005.1452412","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fuzzy.2005.1452412","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The 14th IEEE International Conference on Fuzzy Systems, 2005. FUZZ '05.","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/12","score":0.47999998927116394,"display_name":"Responsible consumption and production"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320306078","display_name":"U.S. Department of Defense","ror":"https://ror.org/0447fe631"},{"id":"https://openalex.org/F4320332180","display_name":"Defense Advanced Research Projects Agency","ror":"https://ror.org/02caytj08"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W153832914","https://openalex.org/W177891611","https://openalex.org/W1483625074","https://openalex.org/W1484478089","https://openalex.org/W1567451061","https://openalex.org/W1571028864","https://openalex.org/W1583887460","https://openalex.org/W1602306524","https://openalex.org/W1689445748","https://openalex.org/W1781059203","https://openalex.org/W2019529630","https://openalex.org/W2046618519","https://openalex.org/W2109015976","https://openalex.org/W2137669384","https://openalex.org/W2141647566","https://openalex.org/W2232413208","https://openalex.org/W4294553839","https://openalex.org/W6628828686","https://openalex.org/W6634125772"],"related_works":["https://openalex.org/W2366107444","https://openalex.org/W4388145910","https://openalex.org/W2381570729","https://openalex.org/W1976205134","https://openalex.org/W4248336175","https://openalex.org/W2031260042","https://openalex.org/W2391445434","https://openalex.org/W3009369890","https://openalex.org/W4312490297","https://openalex.org/W2032548952"],"abstract_inverted_index":{"We":[0,72,131],"analyze":[1],"the":[2,6,34,54,68,79,85,94,99,113,120,128,133,136,156,165,169,176,179],"task":[3],"of":[4,13,26,36,87,119,122,135,142,150,161,171,178],"selecting":[5],"most":[7],"reliable":[8],"units":[9,123],"within":[10],"a":[11,19,148],"fleet":[12,149],"vehicles":[14,40],"and":[15,21,44,84,144,159],"formulate":[16],"it":[17],"as":[18],"prediction":[20,25],"classification":[22],"problem.":[23],"The":[24,117,153],"each":[27,88],"unit's":[28,69,114],"remaining":[29,70,115],"life":[30,126],"is":[31,109],"based":[32],"on":[33],"identification":[35],"\"peer\"":[37],"units,":[38],"i.e.":[39],"with":[41,93,124],"similar":[42],"utilization":[43],"maintenance":[45,145],"records":[46],"that":[47,108],"are":[48],"expected":[49],"to":[50,53,66,77,111],"behave":[51],"similarly":[52],"unit":[55],"under":[56],"consideration.":[57],"With":[58],"these":[59],"peers,":[60],"we":[61,167],"construct":[62],"local":[63],"predictive":[64],"models":[65,181],"estimate":[67],"life.":[71,116],"use":[73],"evolutionary":[74,137],"algorithms":[75],"(EAs)":[76],"develop":[78],"criteria":[80],"for":[81,147,174],"defining":[82],"peers":[83],"relevance":[86],"criterion":[89],"in":[90,98],"evaluating":[91],"similarity":[92],"unit.":[95],"Each":[96],"individual":[97],"EAs":[100],"population":[101],"fully":[102],"characterizes":[103],"an":[104],"instance-based":[105],"fuzzy":[106,180],"model":[107],"used":[110],"predict":[112],"precision":[118],"selection":[121],"best-expected":[125],"provides":[127],"fitness":[129],"value.":[130],"analyzed":[132],"performance":[134],"approach":[138,173],"over":[139],"two":[140],"years":[141],"operation":[143],"data":[146],"1100":[151],"locomotives.":[152],"results":[154],"illustrate":[155],"high":[157],"accuracy":[158],"robustness":[160],"this":[162,172],"approach.":[163],"In":[164],"conclusion,":[166],"highlight":[168],"implications":[170],"supporting":[175],"lifecycle":[177]},"counts_by_year":[{"year":2020,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":2},{"year":2015,"cited_by_count":3},{"year":2014,"cited_by_count":3},{"year":2013,"cited_by_count":2},{"year":2012,"cited_by_count":4}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}