{"id":"https://openalex.org/W7125006354","doi":"https://doi.org/10.1109/access.2026.3654958","title":"Adaptive Segmentation of IIoT Time Series Data via Change Point Detection for Machinery Fault Classification","display_name":"Adaptive Segmentation of IIoT Time Series Data via Change Point Detection for Machinery Fault Classification","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7125006354","doi":"https://doi.org/10.1109/access.2026.3654958"},"language":null,"primary_location":{"id":"doi:10.1109/access.2026.3654958","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3654958","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2026.3654958","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5053940414","display_name":"Tu\u011f\u00e7e Ball\u0131","orcid":"https://orcid.org/0000-0002-6509-3725"},"institutions":[{"id":"https://openalex.org/I132286405","display_name":"Kadir Has University","ror":"https://ror.org/03zzckc47","country_code":"TR","type":"education","lineage":["https://openalex.org/I132286405"]}],"countries":["TR"],"is_corresponding":true,"raw_author_name":"Tu\u011f\u00e7e Balli","raw_affiliation_strings":["Department of Management Information Systems, Kadir Has University, Istanbul, Türkiye"],"raw_orcid":"https://orcid.org/0000-0002-6509-3725","affiliations":[{"raw_affiliation_string":"Department of Management Information Systems, Kadir Has University, Istanbul, Türkiye","institution_ids":["https://openalex.org/I132286405"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5123389120","display_name":"Saygin Ka\u00e7ar","orcid":null},"institutions":[{"id":"https://openalex.org/I132286405","display_name":"Kadir Has University","ror":"https://ror.org/03zzckc47","country_code":"TR","type":"education","lineage":["https://openalex.org/I132286405"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"Saygin Ka\u00e7ar","raw_affiliation_strings":["Department of Management Information Systems, Kadir Has University, Istanbul, Türkiye"],"raw_orcid":"https://orcid.org/0000-0003-4305-9902","affiliations":[{"raw_affiliation_string":"Department of Management Information Systems, Kadir Has University, Istanbul, Türkiye","institution_ids":["https://openalex.org/I132286405"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5123398619","display_name":"E. Fatih Yetkin","orcid":null},"institutions":[{"id":"https://openalex.org/I132286405","display_name":"Kadir Has University","ror":"https://ror.org/03zzckc47","country_code":"TR","type":"education","lineage":["https://openalex.org/I132286405"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"E. Fatih Yetkin","raw_affiliation_strings":["Department of Management Information Systems, Kadir Has University, Istanbul, Türkiye"],"raw_orcid":"https://orcid.org/0000-0003-1115-4454","affiliations":[{"raw_affiliation_string":"Department of Management Information Systems, Kadir Has University, Istanbul, Türkiye","institution_ids":["https://openalex.org/I132286405"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5053940414"],"corresponding_institution_ids":["https://openalex.org/I132286405"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.12561275,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"14","issue":null,"first_page":"10540","last_page":"10551"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.8837000131607056,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.8837000131607056,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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":0.05559999868273735,"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/T10876","display_name":"Fault Detection and Control Systems","score":0.010200000368058681,"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/segmentation","display_name":"Segmentation","score":0.7184000015258789},{"id":"https://openalex.org/keywords/fault-detection-and-isolation","display_name":"Fault detection and isolation","score":0.6624000072479248},{"id":"https://openalex.org/keywords/change-detection","display_name":"Change detection","score":0.642799973487854},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5593000054359436},{"id":"https://openalex.org/keywords/fault","display_name":"Fault (geology)","score":0.4453999996185303},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.42719998955726624},{"id":"https://openalex.org/keywords/condition-monitoring","display_name":"Condition monitoring","score":0.4235000014305115},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.41510000824928284},{"id":"https://openalex.org/keywords/binary-classification","display_name":"Binary classification","score":0.41370001435279846}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8095999956130981},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7184000015258789},{"id":"https://openalex.org/C152745839","wikidata":"https://www.wikidata.org/wiki/Q5438153","display_name":"Fault detection and isolation","level":3,"score":0.6624000072479248},{"id":"https://openalex.org/C203595873","wikidata":"https://www.wikidata.org/wiki/Q25389927","display_name":"Change detection","level":2,"score":0.642799973487854},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5593000054359436},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5435000061988831},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5034000277519226},{"id":"https://openalex.org/C175551986","wikidata":"https://www.wikidata.org/wiki/Q47089","display_name":"Fault (geology)","level":2,"score":0.4453999996185303},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.43130001425743103},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.42719998955726624},{"id":"https://openalex.org/C2775846686","wikidata":"https://www.wikidata.org/wiki/Q643012","display_name":"Condition monitoring","level":2,"score":0.4235000014305115},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.41510000824928284},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.41370001435279846},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.40369999408721924},{"id":"https://openalex.org/C70452415","wikidata":"https://www.wikidata.org/wiki/Q3182448","display_name":"Predictive maintenance","level":2,"score":0.38769999146461487},{"id":"https://openalex.org/C2779466056","wikidata":"https://www.wikidata.org/wiki/Q107630651","display_name":"Time point","level":2,"score":0.38670000433921814},{"id":"https://openalex.org/C110083411","wikidata":"https://www.wikidata.org/wiki/Q1744628","display_name":"Statistical classification","level":2,"score":0.3345000147819519},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.326200008392334},{"id":"https://openalex.org/C2780724565","wikidata":"https://www.wikidata.org/wiki/Q5227256","display_name":"Data classification","level":2,"score":0.30630001425743103},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.304500013589859},{"id":"https://openalex.org/C34872919","wikidata":"https://www.wikidata.org/wiki/Q7092302","display_name":"One-class classification","level":3,"score":0.29649999737739563},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.2955999970436096},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.28119999170303345},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.2653999924659729},{"id":"https://openalex.org/C60777511","wikidata":"https://www.wikidata.org/wiki/Q3045002","display_name":"Concept drift","level":3,"score":0.25929999351501465},{"id":"https://openalex.org/C21080849","wikidata":"https://www.wikidata.org/wiki/Q13611879","display_name":"Data point","level":2,"score":0.2558000087738037}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/access.2026.3654958","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3654958","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1109/access.2026.3654958","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3654958","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","score":0.5688883066177368,"display_name":"Industry, innovation and infrastructure"}],"awards":[{"id":"https://openalex.org/G8522172525","display_name":null,"funder_award_id":"221N220","funder_id":"https://openalex.org/F4320322626","funder_display_name":"T\u00fcrkiye Bilimsel ve Teknolojik Ara\u015ft\u0131rma Kurumu"}],"funders":[{"id":"https://openalex.org/F4320322626","display_name":"T\u00fcrkiye Bilimsel ve Teknolojik Ara\u015ft\u0131rma Kurumu","ror":"https://ror.org/04w9kkr77"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W1975684011","https://openalex.org/W1992222322","https://openalex.org/W2003554623","https://openalex.org/W2047287351","https://openalex.org/W2049723111","https://openalex.org/W2107633943","https://openalex.org/W2118673550","https://openalex.org/W2161336494","https://openalex.org/W2172697690","https://openalex.org/W2183604709","https://openalex.org/W2515822248","https://openalex.org/W2767115086","https://openalex.org/W2909693411","https://openalex.org/W2972137370","https://openalex.org/W2975898216","https://openalex.org/W2998531464","https://openalex.org/W3081235973","https://openalex.org/W3083789190","https://openalex.org/W3096568464","https://openalex.org/W3112955596","https://openalex.org/W3120480454","https://openalex.org/W3166382720","https://openalex.org/W3202361779","https://openalex.org/W3215450491","https://openalex.org/W4235292672","https://openalex.org/W4306654983","https://openalex.org/W4311702840","https://openalex.org/W4381685981","https://openalex.org/W4386558242","https://openalex.org/W4389386902","https://openalex.org/W4390613913","https://openalex.org/W4393029600","https://openalex.org/W4396217140","https://openalex.org/W4396946675","https://openalex.org/W4399729421","https://openalex.org/W4400489941","https://openalex.org/W4405272781","https://openalex.org/W4405305993"],"related_works":[],"abstract_inverted_index":{"Predictive":[0],"maintenance":[1,39],"(PdM)":[2],"is":[3,53,185],"a":[4],"critical":[5,47],"concept":[6],"in":[7,69,195],"Industry":[8],"4.0":[9],"that":[10,144],"aims":[11],"to":[12,32,92,111,139,147],"improve":[13],"manufacturing":[14],"processes":[15],"by":[16,187],"predicting":[17],"the":[18,46,54,72,84,113,121,152,156,182,196],"remaining":[19],"useful":[20],"time":[21,61,77,117],"of":[22,26,45,49,56,75,86,116,124,155,181],"machinery.":[23],"The":[24,179],"development":[25],"PdM":[27,51,197],"models":[28],"relies":[29],"on":[30],"access":[31],"sufficient":[33],"data,":[34],"including":[35],"condition":[36],"monitoring":[37],"and":[38,176],"data":[40,63,79,96],"from":[41,65],"industrial":[42],"applications.":[43],"One":[44],"aspects":[48],"modern":[50],"approaches":[52],"classification":[55,122,149],"potential":[57],"fault":[58,128],"signals":[59],"using":[60,132,189],"series":[62,78],"collected":[64],"IoT":[66],"devices.":[67],"However,":[68],"most":[70],"cases,":[71],"non-stationary":[73],"nature":[74,154],"these":[76],"often":[80],"causes":[81],"difficulties":[82],"with":[83],"validity":[85],"traditional":[87,125],"segmentation":[88,105,135],"techniques":[89],"when":[90],"applied":[91],"such":[93],"dynamically":[94],"changing":[95],"patterns.":[97],"In":[98],"this":[99],"work,":[100],"we":[101,137,160],"propose":[102],"an":[103,133],"adaptive":[104,134],"approach":[106],"through":[107],"change":[108,164],"point":[109,165],"detection":[110,129,166],"address":[112],"inherent":[114],"non-stationarity":[115],"series,":[118],"thereby":[119],"improving":[120],"performance":[123],"classifiers":[126],"for":[127],"problems.":[130],"By":[131],"scheme,":[136],"aim":[138],"extract":[140],"more":[141],"relevant":[142],"features":[143],"will":[145],"lead":[146],"improved":[148],"performance.":[150],"Taking":[151],"time-sensitive":[153],"problem":[157],"into":[158],"account,":[159],"employed":[161],"three":[162],"well-known":[163],"algorithms":[167],"(Pruned":[168],"Exact":[169],"Linear":[170],"Time":[171],"-PELT-":[172],"algorithm,":[173],"Binary":[174],"Segmentation,":[175],"Bottom-up":[177],"segmentation).":[178],"effectiveness":[180],"proposed":[183],"methods":[184],"demonstrated":[186],"experiments":[188],"two":[190],"different":[191],"datasets":[192],"widely":[193],"used":[194],"literature.":[198]},"counts_by_year":[],"updated_date":"2026-01-26T23:06:41.788003","created_date":"2026-01-21T00:00:00"}