{"id":"https://openalex.org/W2987198077","doi":"https://doi.org/10.1115/1.4045445","title":"Failure Prognosis of Complex Equipment With Multistream Deep Recurrent Neural Network","display_name":"Failure Prognosis of Complex Equipment With Multistream Deep Recurrent Neural Network","publication_year":2019,"publication_date":"2019-11-12","ids":{"openalex":"https://openalex.org/W2987198077","doi":"https://doi.org/10.1115/1.4045445","mag":"2987198077"},"language":"en","primary_location":{"id":"doi:10.1115/1.4045445","is_oa":false,"landing_page_url":"https://doi.org/10.1115/1.4045445","pdf_url":null,"source":{"id":"https://openalex.org/S173178594","display_name":"Journal of Computing and Information Science in Engineering","issn_l":"1530-9827","issn":["1530-9827","1944-7078"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310316053","host_organization_name":"ASM International","host_organization_lineage":["https://openalex.org/P4310316053"],"host_organization_lineage_names":["ASM International"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Computing and Information Science in Engineering","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/A5037418316","display_name":"Yonghe Su","orcid":"https://orcid.org/0000-0002-1075-2025"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yonghe Su","raw_affiliation_strings":["School of Automation Science & Electrical Engineering, Beihang University, Beijing 100191, China"],"affiliations":[{"raw_affiliation_string":"School of Automation Science & Electrical Engineering, Beihang University, Beijing 100191, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072705483","display_name":"Fei Tao","orcid":"https://orcid.org/0000-0002-9020-0633"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fei Tao","raw_affiliation_strings":["School of Automation Science & Electrical Engineering, Beihang University, Beijing 100191, China"],"affiliations":[{"raw_affiliation_string":"School of Automation Science & Electrical Engineering, Beihang University, Beijing 100191, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009804612","display_name":"Jian Jin","orcid":null},"institutions":[{"id":"https://openalex.org/I25254941","display_name":"Beijing Normal University","ror":"https://ror.org/022k4wk35","country_code":"CN","type":"education","lineage":["https://openalex.org/I25254941"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jian Jin","raw_affiliation_strings":["Department of Information Management, Beijing Normal University, Beijing 100875, China"],"affiliations":[{"raw_affiliation_string":"Department of Information Management, Beijing Normal University, Beijing 100875, China","institution_ids":["https://openalex.org/I25254941"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100428385","display_name":"Tian Wang","orcid":"https://orcid.org/0000-0001-8427-4495"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tian Wang","raw_affiliation_strings":["School of Automation Science & Electrical Engineering, Beihang University, Beijing 100191, China"],"affiliations":[{"raw_affiliation_string":"School of Automation Science & Electrical Engineering, Beihang University, Beijing 100191, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100682407","display_name":"Qing\u2010Guo Wang","orcid":"https://orcid.org/0000-0002-3672-3716"},"institutions":[{"id":"https://openalex.org/I24027795","display_name":"University of Johannesburg","ror":"https://ror.org/04z6c2n17","country_code":"ZA","type":"education","lineage":["https://openalex.org/I24027795"]}],"countries":["ZA"],"is_corresponding":false,"raw_author_name":"Qingguo Wang","raw_affiliation_strings":["Institute of Intelligent System, University of Johannesburg, Johannesburg, Gauteng ZA 2000, South Africa"],"affiliations":[{"raw_affiliation_string":"Institute of Intelligent System, University of Johannesburg, Johannesburg, Gauteng ZA 2000, South Africa","institution_ids":["https://openalex.org/I24027795"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100435995","display_name":"Lei Wang","orcid":"https://orcid.org/0000-0002-7014-2149"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lei Wang","raw_affiliation_strings":["School of Automation Science & Electrical Engineering, Beihang University, Beijing 100191, China"],"affiliations":[{"raw_affiliation_string":"School of Automation Science & Electrical Engineering, Beihang University, Beijing 100191, China","institution_ids":["https://openalex.org/I82880672"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5037418316"],"corresponding_institution_ids":["https://openalex.org/I82880672"],"apc_list":null,"apc_paid":null,"fwci":1.8535,"has_fulltext":false,"cited_by_count":16,"citation_normalized_percentile":{"value":0.86095483,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"20","issue":"2","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.9990000128746033,"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/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.9990000128746033,"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/T13891","display_name":"Engineering Diagnostics and Reliability","score":0.9933000206947327,"subfield":{"id":"https://openalex.org/subfields/2211","display_name":"Mechanics of Materials"},"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":0.9886999726295471,"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/prognostics","display_name":"Prognostics","score":0.8037421703338623},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6620899438858032},{"id":"https://openalex.org/keywords/normalization","display_name":"Normalization (sociology)","score":0.6253212094306946},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6065958738327026},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5726319551467896},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5436186194419861},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.49378475546836853},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4727512300014496},{"id":"https://openalex.org/keywords/database-normalization","display_name":"Database normalization","score":0.45514586567878723},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.4467664957046509},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.43618693947792053},{"id":"https://openalex.org/keywords/fuse","display_name":"Fuse (electrical)","score":0.4358299970626831},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3862881064414978},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.3104914128780365}],"concepts":[{"id":"https://openalex.org/C129364497","wikidata":"https://www.wikidata.org/wiki/Q3042561","display_name":"Prognostics","level":2,"score":0.8037421703338623},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6620899438858032},{"id":"https://openalex.org/C136886441","wikidata":"https://www.wikidata.org/wiki/Q926129","display_name":"Normalization (sociology)","level":2,"score":0.6253212094306946},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6065958738327026},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5726319551467896},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5436186194419861},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.49378475546836853},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4727512300014496},{"id":"https://openalex.org/C162984825","wikidata":"https://www.wikidata.org/wiki/Q339072","display_name":"Database normalization","level":3,"score":0.45514586567878723},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.4467664957046509},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.43618693947792053},{"id":"https://openalex.org/C141353440","wikidata":"https://www.wikidata.org/wiki/Q182221","display_name":"Fuse (electrical)","level":2,"score":0.4358299970626831},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3862881064414978},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.3104914128780365},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","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/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C19165224","wikidata":"https://www.wikidata.org/wiki/Q23404","display_name":"Anthropology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1115/1.4045445","is_oa":false,"landing_page_url":"https://doi.org/10.1115/1.4045445","pdf_url":null,"source":{"id":"https://openalex.org/S173178594","display_name":"Journal of Computing and Information Science in Engineering","issn_l":"1530-9827","issn":["1530-9827","1944-7078"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310316053","host_organization_name":"ASM International","host_organization_lineage":["https://openalex.org/P4310316053"],"host_organization_lineage_names":["ASM International"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Computing and Information Science in Engineering","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":0.4399999976158142}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":48,"referenced_works":["https://openalex.org/W993929392","https://openalex.org/W1969150846","https://openalex.org/W1978965153","https://openalex.org/W2009216626","https://openalex.org/W2010378328","https://openalex.org/W2020041214","https://openalex.org/W2022561873","https://openalex.org/W2045186954","https://openalex.org/W2053910817","https://openalex.org/W2064675550","https://openalex.org/W2093849451","https://openalex.org/W2106544870","https://openalex.org/W2108898839","https://openalex.org/W2109539992","https://openalex.org/W2115306935","https://openalex.org/W2157457477","https://openalex.org/W2165451779","https://openalex.org/W2287972354","https://openalex.org/W2319025975","https://openalex.org/W2336137070","https://openalex.org/W2342662179","https://openalex.org/W2463813940","https://openalex.org/W2551714397","https://openalex.org/W2591055632","https://openalex.org/W2591818336","https://openalex.org/W2592062672","https://openalex.org/W2601486059","https://openalex.org/W2607131574","https://openalex.org/W2726451741","https://openalex.org/W2736552576","https://openalex.org/W2749684264","https://openalex.org/W2788276261","https://openalex.org/W2789676998","https://openalex.org/W2810112029","https://openalex.org/W2810292802","https://openalex.org/W2885027023","https://openalex.org/W2888320614","https://openalex.org/W2896627015","https://openalex.org/W2913221680","https://openalex.org/W2914488306","https://openalex.org/W2951069449","https://openalex.org/W2953084276","https://openalex.org/W2956309146","https://openalex.org/W3101640299","https://openalex.org/W4240485910","https://openalex.org/W4240996198","https://openalex.org/W6704302202","https://openalex.org/W6743529231"],"related_works":["https://openalex.org/W4241705247","https://openalex.org/W2145362441","https://openalex.org/W2101622345","https://openalex.org/W3134304369","https://openalex.org/W2582603276","https://openalex.org/W2175373041","https://openalex.org/W4386281023","https://openalex.org/W4312138714","https://openalex.org/W2113286112","https://openalex.org/W2012771827"],"abstract_inverted_index":{"Abstract":[0],"The":[1,15,26,120],"failure":[2,24,66,118],"prognosis":[3,144],"is":[4,18,61,111,125,168],"crucial":[5],"for":[6,23,117],"industrial":[7],"equipment":[8],"in":[9],"prognostics":[10],"and":[11,50,90,95,135,170],"health":[12],"management":[13],"field.":[14],"vibration":[16,39,83],"signal":[17,40],"the":[19,34,38,43,65,68,140,149,161,165],"commonly":[20],"used":[21],"data":[22,45,96,151],"prognosis.":[25],"conventional":[27],"prognostic":[28],"approaches":[29,145],"have":[30],"limitations":[31],"to":[32,63,113],"handle":[33],"features":[35,79,87,116],"extracted":[36,81],"from":[37,82,152],"because":[41],"of":[42,67,74,164],"large":[44,154],"quantity,":[46],"complex":[47,69],"feature":[48],"relations,":[49],"limited":[51],"degeneration":[52],"mechanisms.":[53],"In":[54],"this":[55],"paper,":[56],"a":[57,100],"deep":[58,101,106,122],"learning-based":[59],"approach":[60,167],"proposed":[62,141],"predict":[64],"equipment.":[70],"To":[71,138],"supply":[72],"plenty":[73],"features,":[75],"three":[76],"different":[77],"domain":[78],"are":[80,88,146],"signals.":[84],"Next,":[85],"these":[86,115],"preprocessed":[89],"reconstructed":[91],"by":[92],"arctangent":[93],"normalization":[94],"stream,":[97],"respectively.":[98],"Finally,":[99],"neural":[102,108,123],"network,":[103],"namely,":[104],"multistream":[105],"recurrent":[107,128],"network":[109,124],"(MS-DRNN)":[110],"built":[112],"fuse":[114],"target.":[119],"presented":[121],"hybrid,":[126],"involving":[127],"layer,":[129,131,134],"fusion":[130],"fully":[132],"connected":[133],"linear":[136],"layer.":[137],"benchmark":[139],"approach,":[142],"several":[143],"evaluated":[147],"with":[148],"testing":[150],"six":[153],"bearing":[155],"datasets.":[156],"Simulation":[157],"results":[158],"demonstrate":[159],"that":[160],"prediction":[162],"performance":[163],"MS-DRNN-based":[166],"effective":[169],"reliable.":[171]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":6},{"year":2020,"cited_by_count":4}],"updated_date":"2026-04-04T08:04:53.788161","created_date":"2025-10-10T00:00:00"}
