{"id":"https://openalex.org/W7131383569","doi":"https://doi.org/10.1109/tii.2026.3664090","title":"Predictive Monitoring of Industrial Processes and Quality Indices via Joint Training of Soft Sensor and Time-Series Forecasting","display_name":"Predictive Monitoring of Industrial Processes and Quality Indices via Joint Training of Soft Sensor and Time-Series Forecasting","publication_year":2026,"publication_date":"2026-02-25","ids":{"openalex":"https://openalex.org/W7131383569","doi":"https://doi.org/10.1109/tii.2026.3664090"},"language":null,"primary_location":{"id":"doi:10.1109/tii.2026.3664090","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tii.2026.3664090","pdf_url":null,"source":{"id":"https://openalex.org/S184777250","display_name":"IEEE Transactions on Industrial Informatics","issn_l":"1551-3203","issn":["1551-3203","1941-0050"],"is_oa":false,"is_in_doaj":false,"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Industrial Informatics","raw_type":"journal-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/A5004961118","display_name":"Yonghang Li","orcid":null},"institutions":[{"id":"https://openalex.org/I39333907","display_name":"Yanshan University","ror":"https://ror.org/02txfnf15","country_code":"CN","type":"education","lineage":["https://openalex.org/I39333907"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yonghang Li","raw_affiliation_strings":["School of Electrical Engineering, Yanshan University, Qinhuangdao, China"],"raw_orcid":"https://orcid.org/0000-0002-6531-636X","affiliations":[{"raw_affiliation_string":"School of Electrical Engineering, Yanshan University, Qinhuangdao, China","institution_ids":["https://openalex.org/I39333907"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5122870536","display_name":"Xiaochen Hao","orcid":null},"institutions":[{"id":"https://openalex.org/I39333907","display_name":"Yanshan University","ror":"https://ror.org/02txfnf15","country_code":"CN","type":"education","lineage":["https://openalex.org/I39333907"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaochen Hao","raw_affiliation_strings":["School of Electrical Engineering, Yanshan University, Qinhuangdao, China"],"raw_orcid":"https://orcid.org/0000-0001-6948-0995","affiliations":[{"raw_affiliation_string":"School of Electrical Engineering, Yanshan University, Qinhuangdao, China","institution_ids":["https://openalex.org/I39333907"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021214707","display_name":"Xunian Yang","orcid":"https://orcid.org/0000-0001-6517-5096"},"institutions":[{"id":"https://openalex.org/I39333907","display_name":"Yanshan University","ror":"https://ror.org/02txfnf15","country_code":"CN","type":"education","lineage":["https://openalex.org/I39333907"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xunian Yang","raw_affiliation_strings":["School of Electrical Engineering, Yanshan University, Qinhuangdao, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical Engineering, Yanshan University, Qinhuangdao, China","institution_ids":["https://openalex.org/I39333907"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076259305","display_name":"Xingzhi Zheng","orcid":null},"institutions":[{"id":"https://openalex.org/I39333907","display_name":"Yanshan University","ror":"https://ror.org/02txfnf15","country_code":"CN","type":"education","lineage":["https://openalex.org/I39333907"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xingzhi Zheng","raw_affiliation_strings":["School of Electrical Engineering, Yanshan University, Qinhuangdao, China"],"raw_orcid":"https://orcid.org/0009-0009-1425-294X","affiliations":[{"raw_affiliation_string":"School of Electrical Engineering, Yanshan University, Qinhuangdao, China","institution_ids":["https://openalex.org/I39333907"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100589242","display_name":"Libin Wei","orcid":null},"institutions":[{"id":"https://openalex.org/I39333907","display_name":"Yanshan University","ror":"https://ror.org/02txfnf15","country_code":"CN","type":"education","lineage":["https://openalex.org/I39333907"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Libin Wei","raw_affiliation_strings":["School of Electrical Engineering, Yanshan University, Qinhuangdao, China"],"raw_orcid":"https://orcid.org/0009-0008-9415-4874","affiliations":[{"raw_affiliation_string":"School of Electrical Engineering, Yanshan University, Qinhuangdao, China","institution_ids":["https://openalex.org/I39333907"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I39333907"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.1872795,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"22","issue":"6","first_page":"5254","last_page":"5265"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10876","display_name":"Fault Detection and Control Systems","score":0.3691999912261963,"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":0.3691999912261963,"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/T11443","display_name":"Advanced Statistical Process Monitoring","score":0.25040000677108765,"subfield":{"id":"https://openalex.org/subfields/1804","display_name":"Statistics, Probability and Uncertainty"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11667","display_name":"Advanced Chemical Sensor Technologies","score":0.035100001841783524,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical 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/component","display_name":"Component (thermodynamics)","score":0.6694999933242798},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5572999715805054},{"id":"https://openalex.org/keywords/soft-sensor","display_name":"Soft sensor","score":0.5536999702453613},{"id":"https://openalex.org/keywords/joint","display_name":"Joint (building)","score":0.5401999950408936},{"id":"https://openalex.org/keywords/adaptive-sampling","display_name":"Adaptive sampling","score":0.4875999987125397},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4641000032424927},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.42590001225471497},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.42260000109672546}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7516999840736389},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.6694999933242798},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5572999715805054},{"id":"https://openalex.org/C115575686","wikidata":"https://www.wikidata.org/wiki/Q18822403","display_name":"Soft sensor","level":3,"score":0.5536999702453613},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.5401999950408936},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.49390000104904175},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4884999990463257},{"id":"https://openalex.org/C2781395549","wikidata":"https://www.wikidata.org/wiki/Q4680762","display_name":"Adaptive sampling","level":3,"score":0.4875999987125397},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4641000032424927},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.45210000872612},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.42590001225471497},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.42260000109672546},{"id":"https://openalex.org/C151319957","wikidata":"https://www.wikidata.org/wiki/Q752739","display_name":"Asynchronous communication","level":2,"score":0.4034999907016754},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.3971000015735626},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.3716000020503998},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.36730000376701355},{"id":"https://openalex.org/C24590314","wikidata":"https://www.wikidata.org/wiki/Q336038","display_name":"Wireless sensor network","level":2,"score":0.35100001096725464},{"id":"https://openalex.org/C177606310","wikidata":"https://www.wikidata.org/wiki/Q5674297","display_name":"Adaptability","level":2,"score":0.34850001335144043},{"id":"https://openalex.org/C2775846686","wikidata":"https://www.wikidata.org/wiki/Q643012","display_name":"Condition monitoring","level":2,"score":0.2840000092983246},{"id":"https://openalex.org/C24756922","wikidata":"https://www.wikidata.org/wiki/Q1757694","display_name":"Data quality","level":3,"score":0.2727000117301941},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.2685999870300293},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.2644999921321869},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.2551000118255615}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tii.2026.3664090","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tii.2026.3664090","pdf_url":null,"source":{"id":"https://openalex.org/S184777250","display_name":"IEEE Transactions on Industrial Informatics","issn_l":"1551-3203","issn":["1551-3203","1941-0050"],"is_oa":false,"is_in_doaj":false,"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Industrial Informatics","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.5251684188842773}],"awards":[{"id":"https://openalex.org/G7684738659","display_name":null,"funder_award_id":"62073281","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8758957212","display_name":null,"funder_award_id":"F2022203088","funder_id":"https://openalex.org/F4320322163","funder_display_name":"Natural Science Foundation of Hebei Province"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320322163","display_name":"Natural Science Foundation of Hebei Province","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W2539756354","https://openalex.org/W2920714358","https://openalex.org/W2962069445","https://openalex.org/W2979184896","https://openalex.org/W3085046840","https://openalex.org/W3123899295","https://openalex.org/W3128560880","https://openalex.org/W3138516171","https://openalex.org/W3171438403","https://openalex.org/W3177318507","https://openalex.org/W3217429229","https://openalex.org/W4229082741","https://openalex.org/W4285058508","https://openalex.org/W4360994956","https://openalex.org/W4360995494","https://openalex.org/W4382203030","https://openalex.org/W4382203079","https://openalex.org/W4385245566","https://openalex.org/W4385763767","https://openalex.org/W4392828127","https://openalex.org/W4392936942","https://openalex.org/W4397031073","https://openalex.org/W4399392135","https://openalex.org/W4400768333","https://openalex.org/W4400966032","https://openalex.org/W4402916480","https://openalex.org/W4403243056","https://openalex.org/W4405487717","https://openalex.org/W4408399473","https://openalex.org/W7133233229"],"related_works":[],"abstract_inverted_index":{"Predictive":[0],"monitoring":[1,160],"of":[2,17,92,131,142,145,157,168],"industrial":[3],"processes":[4],"and":[5,33,60,109,147,172],"quality":[6,36,110],"indices,":[7],"particularly":[8],"free":[9],"calcium":[10],"oxide":[11],"(f-CaO)":[12],"in":[13],"cement":[14,125],"production,":[15],"is":[16,22],"critical":[18],"importance.":[19],"However,":[20],"it":[21],"significantly":[23],"constrained":[24],"by":[25,112],"the":[26,82,104,117,129,132,148,153,166,169],"asynchronous":[27],"sampling":[28],"between":[29,107,116],"high-frequency":[30],"process":[31,94,108],"data":[32,111],"delayed":[34],"laboratory":[35],"measurements.":[37],"To":[38],"address":[39],"this":[40,42],"challenge,":[41],"article":[43],"proposes":[44],"a":[45,52,61,73,86,123,139],"joint":[46,99],"modeling":[47],"framework":[48],"that":[49],"collaboratively":[50],"optimizes":[51],"multiscale":[53,70],"attention":[54,71],"Kolmogorov\u2013Arnold":[55,74],"network":[56,75],"(MSAKAN)":[57],"soft":[58],"sensor":[59],"MambaMoE":[62,83],"time-series":[63],"forecasting":[64,91],"model.":[65],"The":[66,135],"MSAKAN":[67,136],"component":[68,84],"combines":[69],"with":[72],"to":[76,161],"enhance":[77],"nonlinear":[78],"mapping":[79],"capabilities,":[80],"while":[81],"employs":[85],"mixture-of-experts":[87],"mechanism":[88],"for":[89],"multitask":[90],"future":[93],"variables.":[95],"A":[96],"cascaded":[97],"adversarial-inspired":[98],"training":[100],"strategy":[101],"further":[102],"bridges":[103],"temporal":[105],"disparity":[106],"enabling":[113],"mutual":[114],"adaptation":[115],"two":[118],"models.":[119],"Experimental":[120],"results":[121,164],"on":[122],"real-world":[124],"plant":[126],"dataset":[127],"demonstrate":[128],"effectiveness":[130],"proposed":[133,170],"framework.":[134],"model":[137],"achieves":[138],"pretraining":[140],"coefficient":[141],"determination":[143],"($R^{2}$)":[144],"0.8724,":[146],"jointly":[149],"trained":[150],"system":[151],"reduces":[152],"mean":[154],"absolute":[155],"error":[156],"predictive":[158],"f-CaO":[159],"0.1573.":[162],"These":[163],"confirm":[165],"synergy":[167],"approach":[171],"show":[173],"substantial":[174],"performance":[175],"gains":[176],"over":[177],"state-of-the-art":[178],"baselines.":[179]},"counts_by_year":[],"updated_date":"2026-07-29T09:40:50.615796","created_date":"2026-02-26T00:00:00"}
