{"id":"https://openalex.org/W4205912466","doi":"https://doi.org/10.1109/access.2022.3144333","title":"Deep Learning-Based Fault Prediction in Wireless Sensor Network Embedded Cyber-Physical Systems for Industrial Processes","display_name":"Deep Learning-Based Fault Prediction in Wireless Sensor Network Embedded Cyber-Physical Systems for Industrial Processes","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4205912466","doi":"https://doi.org/10.1109/access.2022.3144333"},"language":"en","primary_location":{"id":"doi:10.1109/access.2022.3144333","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2022.3144333","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/9668973/09684389.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/ielx7/6287639/9668973/09684389.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5014562204","display_name":"Hang Ruan","orcid":null},"institutions":[{"id":"https://openalex.org/I98677209","display_name":"University of Edinburgh","ror":"https://ror.org/01nrxwf90","country_code":"GB","type":"education","lineage":["https://openalex.org/I98677209"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Hang Ruan","raw_affiliation_strings":["School of Mathematics, University of Edinburgh, Edinburgh, U.K"],"raw_orcid":"https://orcid.org/0000-0002-1799-828X","affiliations":[{"raw_affiliation_string":"School of Mathematics, University of Edinburgh, Edinburgh, U.K","institution_ids":["https://openalex.org/I98677209"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081621271","display_name":"Bogdan Dorneanu","orcid":"https://orcid.org/0000-0003-3553-6625"},"institutions":[{"id":"https://openalex.org/I51783024","display_name":"Brandenburg University of Technology Cottbus-Senftenberg","ror":"https://ror.org/02wxx3e24","country_code":"DE","type":"education","lineage":["https://openalex.org/I51783024"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Bogdan Dorneanu","raw_affiliation_strings":["LS Prozess- und Anlagentechnik, Brandenburgische Technische Universitaet Cottbus-Senftenberg, Cottbus, Germany"],"raw_orcid":"https://orcid.org/0000-0003-3553-6625","affiliations":[{"raw_affiliation_string":"LS Prozess- und Anlagentechnik, Brandenburgische Technische Universitaet Cottbus-Senftenberg, Cottbus, Germany","institution_ids":["https://openalex.org/I51783024"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044751389","display_name":"Harvey Arellano\u2010Garc\u00eda","orcid":"https://orcid.org/0000-0002-8297-0232"},"institutions":[{"id":"https://openalex.org/I51783024","display_name":"Brandenburg University of Technology Cottbus-Senftenberg","ror":"https://ror.org/02wxx3e24","country_code":"DE","type":"education","lineage":["https://openalex.org/I51783024"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Harvey Arellano-Garcia","raw_affiliation_strings":["LS Prozess- und Anlagentechnik, Brandenburgische Technische Universitaet Cottbus-Senftenberg, Cottbus, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"LS Prozess- und Anlagentechnik, Brandenburgische Technische Universitaet Cottbus-Senftenberg, Cottbus, Germany","institution_ids":["https://openalex.org/I51783024"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002566516","display_name":"Pei Xiao","orcid":"https://orcid.org/0000-0002-7886-5878"},"institutions":[{"id":"https://openalex.org/I28290843","display_name":"University of Surrey","ror":"https://ror.org/00ks66431","country_code":"GB","type":"education","lineage":["https://openalex.org/I28290843"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Pei Xiao","raw_affiliation_strings":["Institute for Communication Systems, University of Surrey, Guildford, U.K"],"raw_orcid":"https://orcid.org/0000-0002-7886-5878","affiliations":[{"raw_affiliation_string":"Institute for Communication Systems, University of Surrey, Guildford, U.K","institution_ids":["https://openalex.org/I28290843"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100418950","display_name":"Li Zhang","orcid":"https://orcid.org/0000-0001-6674-692X"},"institutions":[{"id":"https://openalex.org/I184558857","display_name":"Royal Holloway University of London","ror":"https://ror.org/04g2vpn86","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I184558857"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Li Zhang","raw_affiliation_strings":["Royal Holloway, University of London, Egham, U.K"],"raw_orcid":"https://orcid.org/0000-0001-6674-692X","affiliations":[{"raw_affiliation_string":"Royal Holloway, University of London, Egham, U.K","institution_ids":["https://openalex.org/I184558857"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":3.5326,"has_fulltext":true,"cited_by_count":43,"citation_normalized_percentile":{"value":0.93272489,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":100},"biblio":{"volume":"10","issue":null,"first_page":"10867","last_page":"10879"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10876","display_name":"Fault Detection and Control Systems","score":0.9991999864578247,"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":0.9991999864578247,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9965999722480774,"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/T11667","display_name":"Advanced Chemical Sensor Technologies","score":0.9883000254631042,"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/computer-science","display_name":"Computer science","score":0.7240299582481384},{"id":"https://openalex.org/keywords/cyber-physical-system","display_name":"Cyber-physical system","score":0.5828110575675964},{"id":"https://openalex.org/keywords/fault","display_name":"Fault (geology)","score":0.5734211206436157},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5237626433372498},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5076156854629517},{"id":"https://openalex.org/keywords/wireless-sensor-network","display_name":"Wireless sensor network","score":0.5044523477554321},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.47462332248687744},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4592888057231903},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4478071630001068},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.36760789155960083},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.09223175048828125}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7240299582481384},{"id":"https://openalex.org/C179768478","wikidata":"https://www.wikidata.org/wiki/Q1120057","display_name":"Cyber-physical system","level":2,"score":0.5828110575675964},{"id":"https://openalex.org/C175551986","wikidata":"https://www.wikidata.org/wiki/Q47089","display_name":"Fault (geology)","level":2,"score":0.5734211206436157},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5237626433372498},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5076156854629517},{"id":"https://openalex.org/C24590314","wikidata":"https://www.wikidata.org/wiki/Q336038","display_name":"Wireless sensor network","level":2,"score":0.5044523477554321},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.47462332248687744},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4592888057231903},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4478071630001068},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.36760789155960083},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.09223175048828125},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C165205528","wikidata":"https://www.wikidata.org/wiki/Q83371","display_name":"Seismology","level":1,"score":0.0},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2022.3144333","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2022.3144333","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/9668973/09684389.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:fa71584fe70a441c99aca94118fe5158","is_oa":true,"landing_page_url":"https://doaj.org/article/fa71584fe70a441c99aca94118fe5158","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"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 10, Pp 10867-10879 (2022)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2022.3144333","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2022.3144333","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/9668973/09684389.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":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.6700000166893005}],"awards":[{"id":"https://openalex.org/G1025925688","display_name":"Stepping towards the industrial 6th Sense","funder_award_id":"EP/R001588/1","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"},{"id":"https://openalex.org/G4512960364","display_name":null,"funder_award_id":"EP/R001588/1","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"}],"funders":[{"id":"https://openalex.org/F4320334627","display_name":"Engineering and Physical Sciences Research Council","ror":"https://ror.org/0439y7842"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4205912466.pdf","grobid_xml":"https://content.openalex.org/works/W4205912466.grobid-xml"},"referenced_works_count":48,"referenced_works":["https://openalex.org/W1993024865","https://openalex.org/W2040491897","https://openalex.org/W2128569883","https://openalex.org/W2323284870","https://openalex.org/W2343814947","https://openalex.org/W2550143307","https://openalex.org/W2551370127","https://openalex.org/W2553509911","https://openalex.org/W2571254578","https://openalex.org/W2608459225","https://openalex.org/W2762538584","https://openalex.org/W2768753204","https://openalex.org/W2796538079","https://openalex.org/W2797302551","https://openalex.org/W2806122632","https://openalex.org/W2825766531","https://openalex.org/W2889045127","https://openalex.org/W2906136421","https://openalex.org/W2906498146","https://openalex.org/W2910886069","https://openalex.org/W2913400796","https://openalex.org/W2920714358","https://openalex.org/W2930036527","https://openalex.org/W2947330499","https://openalex.org/W2948566387","https://openalex.org/W2954637328","https://openalex.org/W2962736999","https://openalex.org/W2978995128","https://openalex.org/W2979467104","https://openalex.org/W2988851462","https://openalex.org/W2990406790","https://openalex.org/W3028461748","https://openalex.org/W3036126481","https://openalex.org/W3041738830","https://openalex.org/W3044479924","https://openalex.org/W3092046242","https://openalex.org/W3093565541","https://openalex.org/W3098015864","https://openalex.org/W3110518539","https://openalex.org/W3116969788","https://openalex.org/W3123265618","https://openalex.org/W3159527824","https://openalex.org/W3183286678","https://openalex.org/W3185553200","https://openalex.org/W3186409582","https://openalex.org/W3195071730","https://openalex.org/W3215095098","https://openalex.org/W6736549786"],"related_works":["https://openalex.org/W3004173571","https://openalex.org/W3019776739","https://openalex.org/W2546638913","https://openalex.org/W2209816623","https://openalex.org/W2968885840","https://openalex.org/W4375867731","https://openalex.org/W3135700974","https://openalex.org/W4313307484","https://openalex.org/W2791379413","https://openalex.org/W2086962923"],"abstract_inverted_index":{"This":[0],"paper":[1],"investigates":[2],"the":[3,24,98,138,182],"challenging":[4],"fault":[5,127,172,176],"prediction":[6,104,150,155,173],"problem":[7],"in":[8,21,148,169],"process":[9],"industries":[10],"that":[11,160],"adopt":[12],"autonomous":[13],"and":[14,33,131,175],"intelligent":[15],"cyber-physical":[16],"systems":[17],"(CPS),":[18],"which":[19],"is":[20,70,95,120,136,162],"line":[22],"with":[23,59,67,117],"emerging":[25],"developments":[26],"of":[27,30,49,171],"industrial":[28],"internet":[29],"things":[31],"(IIoT)":[32],"Industry":[34],"4.0.":[35],"Particularly,":[36],"we":[37],"developed":[38,96],"an":[39],"end-to-end":[40],"deep":[41],"learning":[42,88],"approach":[43],"based":[44,110],"on":[45,75,111],"a":[46,55,63,80,86,107],"large":[47],"volume":[48],"real-time":[50],"sensory":[51],"data":[52,78],"collected":[53],"from":[54],"chemical":[56],"plant":[57],"equipped":[58],"wireless":[60],"sensors.":[61],"Firstly,":[62],"novel":[64,87],"recursive":[65,91],"architecture":[66,100],"multi-lookback":[68],"inputs":[69],"proposed":[71,99,121],"to":[72,101,122,164,181],"perform":[73,123],"autoregression":[74],"imbalanced":[76],"time-series":[77],"as":[79],"preliminary":[81],"prediction.":[82],"In":[83],"this":[84],"process,":[85],"algorithm":[89],"named":[90,137],"gradient":[92],"descent":[93],"(RGD)":[94],"for":[97,126,144],"reduce":[102],"cumulative":[103,139],"uncertainties.":[105],"Subsequently,":[106],"classification":[108,125,178],"model":[109],"temporal":[112],"convolutions":[113],"over":[114,153],"multiple":[115,154],"channels":[116],"decay":[118],"effect":[119],"multi-class":[124],"root":[128],"cause":[129],"identification":[130],"localization.":[132],"The":[133],"overall":[134],"network":[135,142],"uncertainty":[140],"reduction":[141],"(CURNet),":[143],"its":[145],"superior":[146,166],"capacity":[147],"reducing":[149],"uncertainties":[151],"accumulated":[152],"steps.":[156],"Performance":[157],"evaluations":[158],"show":[159],"CURNet":[161],"able":[163],"achieve":[165],"performance":[167],"especially":[168],"terms":[170],"recall":[174],"type":[177],"accuracy,":[179],"compared":[180],"existing":[183],"techniques.":[184]},"counts_by_year":[{"year":2026,"cited_by_count":7},{"year":2025,"cited_by_count":16},{"year":2024,"cited_by_count":10},{"year":2023,"cited_by_count":9},{"year":2022,"cited_by_count":1}],"updated_date":"2026-07-15T18:14:33.161393","created_date":"2025-10-10T00:00:00"}
