{"id":"https://openalex.org/W3034514548","doi":"https://doi.org/10.1109/access.2020.3002257","title":"Segmentation of Multivariate Industrial Time Series Data Based on Dynamic Latent Variable Predictability","display_name":"Segmentation of Multivariate Industrial Time Series Data Based on Dynamic Latent Variable Predictability","publication_year":2020,"publication_date":"2020-01-01","ids":{"openalex":"https://openalex.org/W3034514548","doi":"https://doi.org/10.1109/access.2020.3002257","mag":"3034514548"},"language":"en","primary_location":{"id":"doi:10.1109/access.2020.3002257","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.3002257","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09116988.pdf","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://ieeexplore.ieee.org/ielx7/6287639/8948470/09116988.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5042740204","display_name":"Shaowen Lu","orcid":"https://orcid.org/0000-0003-3537-8620"},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Shaowen Lu","raw_affiliation_strings":["State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang, China"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang, China","institution_ids":["https://openalex.org/I9224756"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103276796","display_name":"Shuyu Huang","orcid":"https://orcid.org/0000-0002-6646-0451"},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuyu Huang","raw_affiliation_strings":["State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang, China"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang, China","institution_ids":["https://openalex.org/I9224756"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5042740204"],"corresponding_institution_ids":["https://openalex.org/I9224756"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":1.4832,"has_fulltext":true,"cited_by_count":17,"citation_normalized_percentile":{"value":0.81827411,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":93,"max":99},"biblio":{"volume":"8","issue":null,"first_page":"112092","last_page":"112103"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10876","display_name":"Fault Detection and Control Systems","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T10876","display_name":"Fault Detection and Control Systems","score":0.9998999834060669,"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/T10791","display_name":"Advanced Control Systems Optimization","score":0.9797999858856201,"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/T14470","display_name":"Advanced Data Processing Techniques","score":0.9775999784469604,"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/predictability","display_name":"Predictability","score":0.8442505598068237},{"id":"https://openalex.org/keywords/latent-variable","display_name":"Latent variable","score":0.7557282447814941},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.7094990015029907},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.658499002456665},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.6293678283691406},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.5877661108970642},{"id":"https://openalex.org/keywords/variable","display_name":"Variable (mathematics)","score":0.4988994598388672},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4368855059146881},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.39354002475738525},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.34870797395706177},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.31590187549591064},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2952537536621094},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1941075325012207}],"concepts":[{"id":"https://openalex.org/C197640229","wikidata":"https://www.wikidata.org/wiki/Q2534066","display_name":"Predictability","level":2,"score":0.8442505598068237},{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.7557282447814941},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.7094990015029907},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.658499002456665},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.6293678283691406},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.5877661108970642},{"id":"https://openalex.org/C182365436","wikidata":"https://www.wikidata.org/wiki/Q50701","display_name":"Variable (mathematics)","level":2,"score":0.4988994598388672},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4368855059146881},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.39354002475738525},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.34870797395706177},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.31590187549591064},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2952537536621094},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1941075325012207},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2020.3002257","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.3002257","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09116988.pdf","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"},{"id":"pmh:oai:doaj.org/article:4941fd892a1c4f73958d06113ef9f980","is_oa":true,"landing_page_url":"https://doaj.org/article/4941fd892a1c4f73958d06113ef9f980","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 8, Pp 112092-112103 (2020)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2020.3002257","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.3002257","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09116988.pdf","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":[{"score":0.5299999713897705,"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9"}],"awards":[{"id":"https://openalex.org/G1231421488","display_name":null,"funder_award_id":"under","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2087396116","display_name":null,"funder_award_id":"China","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3317480652","display_name":null,"funder_award_id":"Science","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G391238517","display_name":null,"funder_award_id":", and","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G40918233","display_name":null,"funder_award_id":"61833004","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5939423041","display_name":null,"funder_award_id":"Technology","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5994120800","display_name":null,"funder_award_id":"Natural","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3034514548.pdf","grobid_xml":"https://content.openalex.org/works/W3034514548.grobid-xml"},"referenced_works_count":38,"referenced_works":["https://openalex.org/W1510320955","https://openalex.org/W1520580650","https://openalex.org/W1547333707","https://openalex.org/W1557694976","https://openalex.org/W1585539308","https://openalex.org/W1969450892","https://openalex.org/W1970537494","https://openalex.org/W1982584963","https://openalex.org/W1983077403","https://openalex.org/W1991145960","https://openalex.org/W2044410124","https://openalex.org/W2045477744","https://openalex.org/W2057053883","https://openalex.org/W2077791644","https://openalex.org/W2107633943","https://openalex.org/W2113360617","https://openalex.org/W2126616170","https://openalex.org/W2142561902","https://openalex.org/W2143288231","https://openalex.org/W2163901641","https://openalex.org/W2171033594","https://openalex.org/W2397022327","https://openalex.org/W2483430316","https://openalex.org/W2515822248","https://openalex.org/W2527895919","https://openalex.org/W2579745982","https://openalex.org/W2626923521","https://openalex.org/W2792033035","https://openalex.org/W2792310042","https://openalex.org/W2802962751","https://openalex.org/W2917825635","https://openalex.org/W2950361482","https://openalex.org/W2963460797","https://openalex.org/W2994908203","https://openalex.org/W2999159787","https://openalex.org/W4289146965","https://openalex.org/W6722243183","https://openalex.org/W6748034048"],"related_works":["https://openalex.org/W2726467123","https://openalex.org/W2064726690","https://openalex.org/W4252678288","https://openalex.org/W4254065731","https://openalex.org/W1607297154","https://openalex.org/W4210820789","https://openalex.org/W2913177154","https://openalex.org/W4237782192","https://openalex.org/W4235131201","https://openalex.org/W4232793539"],"abstract_inverted_index":{"Time":[0],"series":[1,31,48,79,171],"segmentation":[2,146],"is":[3,120,162],"an":[4,40,175],"important":[5],"vehicle":[6],"of":[7,16,62,83,95,118,126,155],"data":[8,89,167,172],"mining":[9],"and":[10,19,91,168],"extensively":[11],"applied":[12],"in":[13,29,88,98],"the":[14,54,60,96,123,127,134,141,145,150,169,185],"areas":[15],"machine":[17],"learning":[18],"anomaly":[20],"detection.":[21],"In":[22,57],"real":[23],"world":[24],"tasks,":[25],"dynamics":[26,55,63,84],"widely":[27],"exist":[28],"time":[30,47,78,170],"but":[32],"have":[33],"been":[34],"little":[35],"concerned.":[36],"This":[37],"paper":[38],"proposes":[39],"algorithm":[41,106,161],"which":[42,130,148],"can":[43,85],"partition":[44],"a":[45,112],"multivariate":[46],"into":[49],"subsequences":[50,110],"at":[51],"points":[52],"where":[53],"change.":[56],"process":[58,97,101],"industries,":[59],"change":[61,82],"often":[64],"relates":[65],"to":[66,76,81],"operation":[67],"regime":[68],"change,":[69],"working":[70],"condition":[71],"shifting":[72],"or":[73],"faults.":[74],"Therefore,":[75],"segment":[77],"according":[80],"be":[86],"useful":[87],"preprocessing":[90],"getting":[92],"deep":[93],"insight":[94],"various":[99],"industrial":[100,176],"monitoring":[102],"tasks.":[103],"The":[104,116,159,179],"proposed":[105,160],"recursively":[107],"merges":[108],"neighborhood":[109],"through":[111],"heuristic":[113],"bottom-up":[114],"procedure.":[115],"cost":[117],"merging":[119],"defined":[121],"as":[122],"mutual":[124],"predictability":[125],"subsequence":[128,157],"models":[129],"are":[131],"constructed":[132],"using":[133],"dynamic-inner":[135],"principal":[136,187],"component":[137,188],"analysis":[138,189],"algorithm.":[139],"Then,":[140],"goal":[142],"becomes":[143],"finding":[144],"scheme":[147],"minimizes":[149],"averaged":[151],"dynamic":[152],"prediction":[153],"errors":[154],"each":[156],"model.":[158],"evaluated":[163],"on":[164],"both":[165],"simulated":[166],"collected":[173],"from":[174],"processing":[177],"plant.":[178],"results":[180],"show":[181],"that":[182],"it":[183],"outperforms":[184],"static":[186],"based":[190],"methods.":[191]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":2}],"updated_date":"2026-03-18T14:38:29.013473","created_date":"2025-10-10T00:00:00"}