{"id":"https://openalex.org/W2186188097","doi":"https://doi.org/10.1109/dsaa.2015.7344806","title":"Predictive reliability mining for early warnings in populations of connected machines","display_name":"Predictive reliability mining for early warnings in populations of connected machines","publication_year":2015,"publication_date":"2015-10-01","ids":{"openalex":"https://openalex.org/W2186188097","doi":"https://doi.org/10.1109/dsaa.2015.7344806","mag":"2186188097"},"language":"en","primary_location":{"id":"doi:10.1109/dsaa.2015.7344806","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dsaa.2015.7344806","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA)","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/A5102172739","display_name":"Karamjit Singh","orcid":null},"institutions":[{"id":"https://openalex.org/I4210104194","display_name":"Tennessee Cancer Specialists","ror":"https://ror.org/01krbfc31","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I4210104194"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Karamjit Singh","raw_affiliation_strings":["TCS Research"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"TCS Research","institution_ids":["https://openalex.org/I4210104194"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038397893","display_name":"Gautam Shroff","orcid":"https://orcid.org/0000-0002-0340-0283"},"institutions":[{"id":"https://openalex.org/I4210104194","display_name":"Tennessee Cancer Specialists","ror":"https://ror.org/01krbfc31","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I4210104194"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Gautam Shroff","raw_affiliation_strings":["TCS Research"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"TCS Research","institution_ids":["https://openalex.org/I4210104194"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103222366","display_name":"Puneet Agarwal","orcid":"https://orcid.org/0000-0002-0063-5079"},"institutions":[{"id":"https://openalex.org/I4210104194","display_name":"Tennessee Cancer Specialists","ror":"https://ror.org/01krbfc31","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I4210104194"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Puneet Agarwal","raw_affiliation_strings":["TCS Research"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"TCS Research","institution_ids":["https://openalex.org/I4210104194"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.1119,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.80758557,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"10"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12423","display_name":"Software Reliability and Analysis Research","score":0.9988999962806702,"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"}},"topics":[{"id":"https://openalex.org/T12423","display_name":"Software Reliability and Analysis Research","score":0.9988999962806702,"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"}},{"id":"https://openalex.org/T10260","display_name":"Software Engineering Research","score":0.9958999752998352,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T10780","display_name":"Reliability and Maintenance Optimization","score":0.9822999835014343,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.7789599895477295},{"id":"https://openalex.org/keywords/warranty","display_name":"Warranty","score":0.7299529314041138},{"id":"https://openalex.org/keywords/weibull-distribution","display_name":"Weibull distribution","score":0.7060562372207642},{"id":"https://openalex.org/keywords/population","display_name":"Population","score":0.6347843408584595},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5801118612289429},{"id":"https://openalex.org/keywords/reliability-engineering","display_name":"Reliability engineering","score":0.4927676022052765},{"id":"https://openalex.org/keywords/warning-system","display_name":"Warning system","score":0.45833805203437805},{"id":"https://openalex.org/keywords/benfords-law","display_name":"Benford's law","score":0.4442657232284546},{"id":"https://openalex.org/keywords/exponential-distribution","display_name":"Exponential distribution","score":0.4118977189064026},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.4107394516468048},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3866311311721802},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.2597687840461731},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.24181586503982544},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.22057059407234192},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.12538409233093262}],"concepts":[{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.7789599895477295},{"id":"https://openalex.org/C2779056723","wikidata":"https://www.wikidata.org/wiki/Q329717","display_name":"Warranty","level":2,"score":0.7299529314041138},{"id":"https://openalex.org/C173291955","wikidata":"https://www.wikidata.org/wiki/Q732332","display_name":"Weibull distribution","level":2,"score":0.7060562372207642},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.6347843408584595},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5801118612289429},{"id":"https://openalex.org/C200601418","wikidata":"https://www.wikidata.org/wiki/Q2193887","display_name":"Reliability engineering","level":1,"score":0.4927676022052765},{"id":"https://openalex.org/C29825287","wikidata":"https://www.wikidata.org/wiki/Q1427940","display_name":"Warning system","level":2,"score":0.45833805203437805},{"id":"https://openalex.org/C152636012","wikidata":"https://www.wikidata.org/wiki/Q817168","display_name":"Benford's law","level":2,"score":0.4442657232284546},{"id":"https://openalex.org/C55350006","wikidata":"https://www.wikidata.org/wiki/Q237193","display_name":"Exponential distribution","level":2,"score":0.4118977189064026},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4107394516468048},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3866311311721802},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.2597687840461731},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.24181586503982544},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.22057059407234192},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.12538409233093262},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"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/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"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/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/dsaa.2015.7344806","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dsaa.2015.7344806","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4300000071525574,"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W170040788","https://openalex.org/W1481308262","https://openalex.org/W1535183290","https://openalex.org/W1540637265","https://openalex.org/W1547387307","https://openalex.org/W1717488553","https://openalex.org/W1830434767","https://openalex.org/W1960881728","https://openalex.org/W2026905639","https://openalex.org/W2071260113","https://openalex.org/W2080708433","https://openalex.org/W2101377431","https://openalex.org/W2105300539","https://openalex.org/W2105647833","https://openalex.org/W2106811752","https://openalex.org/W2111532116","https://openalex.org/W2112249186","https://openalex.org/W2125219410","https://openalex.org/W2125696634","https://openalex.org/W2164463086","https://openalex.org/W2166559705","https://openalex.org/W2250081186","https://openalex.org/W2306314146","https://openalex.org/W2336481247","https://openalex.org/W2473800907","https://openalex.org/W2902455138","https://openalex.org/W4295660562","https://openalex.org/W4297801605","https://openalex.org/W6632022884","https://openalex.org/W6637752956","https://openalex.org/W6675767040","https://openalex.org/W6676740804","https://openalex.org/W6691189252","https://openalex.org/W6703697611","https://openalex.org/W6720537231","https://openalex.org/W6756753118"],"related_works":["https://openalex.org/W2116770244","https://openalex.org/W2778832523","https://openalex.org/W2522228459","https://openalex.org/W2734415684","https://openalex.org/W4384501699","https://openalex.org/W1513619851","https://openalex.org/W1620508096","https://openalex.org/W2357734103","https://openalex.org/W1490217699","https://openalex.org/W3195534432"],"abstract_inverted_index":{"Traditional":[0],"reliability":[1,148,178,257,267],"analysis":[2,149],"of":[3,9,41,59,88,170,199,241,264],"complex":[4],"machinery":[5],"involves":[6],"statistical":[7],"modeling":[8],"historical":[10],"data":[11,273,288],"on":[12,48,73],"part":[13],"failures":[14,32,77,130],"from":[15,20,187,217,289],"warranty":[16],"claims,":[17],"using":[18,176,272,280],"distributions":[19],"exponential":[21],"family":[22],"such":[23,49,69,153,214,234],"as":[24,55,101,124,154,256,284,286],"the":[25,44,65,108,159,200,223,239,262],"Weibull":[26],"or":[27,35,105,206],"log-normal":[28],"distribution.":[29],"When":[30],"observed":[31,132,157],"(in":[33],"one":[34],"more":[36],"parts)":[37],"across":[38,222,243],"a":[39,50,60,141,203,210,228,245,265,290],"population":[40],"machines":[42,87,207],"exceed":[43],"number":[45,240],"expected":[46,188,218],"based":[47],"model,":[51],"this":[52,137],"may":[53,247],"serve":[54,123],"an":[56],"early":[57,70],"warning":[58],"potential":[61,171],"systemic":[62],"problem":[63],"with":[64,93,150,269],"population.":[66,225],"Of":[67],"course,":[68],"warnings":[71],"rely":[72],"some":[74,196],"exceptionally":[75],"high":[76],"having":[78],"actually":[79],"occurred.":[80],"However,":[81],"modern":[82],"connected":[83],"vehicles,":[84],"engines":[85],"and":[86],"all":[89],"kinds":[90],"are":[91,113,131,220],"equipped":[92],"on-board":[94],"electronics":[95],"that":[96,145,164,175,184,232],"transmit":[97],"alerts,":[98],"referred":[99],"to":[100,122],"`diagnostic":[102],"trouble":[103],"codes'":[104],"DTCs":[106],"over":[107,158],"network,":[109],"whenever":[110],"abnormal":[111],"conditions":[112],"detected.":[114],"Such":[115],"DTC":[116,155],"signals":[117,156],"should":[118],"also":[119],"be":[120,248],"able":[121],"early-warning":[125,151],"indicators,":[126],"typically":[127],"before":[128],"actual":[129],"in":[133,195],"large":[134],"numbers.":[135],"In":[136,213],"paper,":[138],"we":[139,182],"develop":[140],"graphical":[142],"Bayesian":[143,266],"model":[144,167,268],"augments":[146],"standard":[147],"indicators":[152],"industrial":[160],"Internet.":[161],"We":[162,226,252,276],"demonstrate":[163],"our":[165,254],"augmented":[166],"can":[168],"detect":[169],"problems":[172],"earlier":[173],"than":[174],"traditional":[177],"analysis.":[179],"Going":[180],"further,":[181],"note":[183],"significant":[185],"deviations":[186,216],"failure":[189],"counts":[190],"might":[191],"often":[192],"occur":[193],"only":[194],"unknown":[197],"subset":[198,246],"population,":[201],"e.g.,":[202],"particular":[204,211],"batch,":[205],"manufactured":[208],"at":[209],"plant.":[212],"cases,":[215],"numbers":[219],"insignificant":[221],"full":[224],"present":[227,277],"rule":[229],"mining":[230,258,274],"technique":[231],"discovers":[233],"subsets":[235],"efficiently":[236],"even":[237],"when":[238],"dimensions":[242],"which":[244],"defined":[249],"is":[250],"large.":[251],"term":[253],"approach":[255],"since":[259],"it":[260],"combines":[261],"use":[263],"subgroup":[270],"discovery":[271],"techniques.":[275],"experimental":[278],"results":[279],"synthetically":[281],"simulated":[282],"scenarios":[283],"well":[285],"real-life":[287],"major":[291],"global":[292],"automobile":[293],"manufacturer.":[294]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2020,"cited_by_count":2},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
