{"id":"https://openalex.org/W2896882406","doi":"https://doi.org/10.3390/s18103521","title":"A Multimodal Feature Fusion-Based Deep Learning Method for Online Fault Diagnosis of Rotating Machinery","display_name":"A Multimodal Feature Fusion-Based Deep Learning Method for Online Fault Diagnosis of Rotating Machinery","publication_year":2018,"publication_date":"2018-10-18","ids":{"openalex":"https://openalex.org/W2896882406","doi":"https://doi.org/10.3390/s18103521","mag":"2896882406","pmid":"https://pubmed.ncbi.nlm.nih.gov/30340412"},"language":"en","primary_location":{"id":"doi:10.3390/s18103521","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s18103521","pdf_url":null,"source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.3390/s18103521","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5079950677","display_name":"Funa Zhou","orcid":"https://orcid.org/0000-0003-3592-9664"},"institutions":[{"id":"https://openalex.org/I173899330","display_name":"Henan University","ror":"https://ror.org/003xyzq10","country_code":"CN","type":"education","lineage":["https://openalex.org/I173899330"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Funa Zhou","raw_affiliation_strings":["School of Computer and Information Engineering, Henan University, Kaifeng 475004, China"],"raw_orcid":"https://orcid.org/0000-0003-3592-9664","affiliations":[{"raw_affiliation_string":"School of Computer and Information Engineering, Henan University, Kaifeng 475004, China","institution_ids":["https://openalex.org/I173899330"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059539259","display_name":"Po Hu","orcid":"https://orcid.org/0000-0002-5859-8100"},"institutions":[{"id":"https://openalex.org/I173899330","display_name":"Henan University","ror":"https://ror.org/003xyzq10","country_code":"CN","type":"education","lineage":["https://openalex.org/I173899330"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Po Hu","raw_affiliation_strings":["School of Computer and Information Engineering, Henan University, Kaifeng 475004, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer and Information Engineering, Henan University, Kaifeng 475004, China","institution_ids":["https://openalex.org/I173899330"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086263105","display_name":"Shuai Yang","orcid":"https://orcid.org/0000-0001-8836-4236"},"institutions":[{"id":"https://openalex.org/I173899330","display_name":"Henan University","ror":"https://ror.org/003xyzq10","country_code":"CN","type":"education","lineage":["https://openalex.org/I173899330"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuai Yang","raw_affiliation_strings":["School of Computer and Information Engineering, Henan University, Kaifeng 475004, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer and Information Engineering, Henan University, Kaifeng 475004, China","institution_ids":["https://openalex.org/I173899330"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5086718724","display_name":"Chenglin Wen","orcid":"https://orcid.org/0000-0003-2471-2849"},"institutions":[{"id":"https://openalex.org/I50760025","display_name":"Hangzhou Dianzi University","ror":"https://ror.org/0576gt767","country_code":"CN","type":"education","lineage":["https://openalex.org/I50760025"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chenglin Wen","raw_affiliation_strings":["School of Automatic, Hangzhou Dianzi University, Hangzhou 310018, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Automatic, Hangzhou Dianzi University, Hangzhou 310018, China","institution_ids":["https://openalex.org/I50760025"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5059539259","https://openalex.org/A5079950677"],"corresponding_institution_ids":["https://openalex.org/I173899330"],"apc_list":{"value":2400,"currency":"CHF","value_usd":2598},"apc_paid":{"value":2400,"currency":"CHF","value_usd":2598},"fwci":2.2474,"has_fulltext":false,"cited_by_count":36,"citation_normalized_percentile":{"value":0.88757889,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":"18","issue":"10","first_page":"3521","last_page":"3521"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.9991000294685364,"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":0.9991000294685364,"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":0.9937999844551086,"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":0.9864000082015991,"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/softmax-function","display_name":"Softmax function","score":0.8503364324569702},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7005696296691895},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6429795026779175},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5776252150535583},{"id":"https://openalex.org/keywords/frequency-domain","display_name":"Frequency domain","score":0.5758649706840515},{"id":"https://openalex.org/keywords/fault","display_name":"Fault (geology)","score":0.5693890452384949},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5692816972732544},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5487369894981384},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5409172177314758},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5274075865745544},{"id":"https://openalex.org/keywords/time-domain","display_name":"Time domain","score":0.5110129117965698},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.487984836101532},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.43002790212631226},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.2147153913974762}],"concepts":[{"id":"https://openalex.org/C188441871","wikidata":"https://www.wikidata.org/wiki/Q7554146","display_name":"Softmax function","level":3,"score":0.8503364324569702},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7005696296691895},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6429795026779175},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5776252150535583},{"id":"https://openalex.org/C19118579","wikidata":"https://www.wikidata.org/wiki/Q786423","display_name":"Frequency domain","level":2,"score":0.5758649706840515},{"id":"https://openalex.org/C175551986","wikidata":"https://www.wikidata.org/wiki/Q47089","display_name":"Fault (geology)","level":2,"score":0.5693890452384949},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5692816972732544},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5487369894981384},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5409172177314758},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5274075865745544},{"id":"https://openalex.org/C103824480","wikidata":"https://www.wikidata.org/wiki/Q185889","display_name":"Time domain","level":2,"score":0.5110129117965698},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.487984836101532},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.43002790212631226},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.2147153913974762},{"id":"https://openalex.org/C165205528","wikidata":"https://www.wikidata.org/wiki/Q83371","display_name":"Seismology","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","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":5,"locations":[{"id":"doi:10.3390/s18103521","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s18103521","pdf_url":null,"source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},{"id":"pmid:30340412","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/30340412","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors (Basel, Switzerland)","raw_type":null},{"id":"pmh:oai:doaj.org/article:c5b1ac8673e044f3a703a7af2240dcec","is_oa":true,"landing_page_url":"https://doaj.org/article/c5b1ac8673e044f3a703a7af2240dcec","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":"Sensors, Vol 18, Iss 10, p 3521 (2018)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/1424-8220/18/10/3521/","is_oa":true,"landing_page_url":"http://dx.doi.org/10.3390/s18103521","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sensors","raw_type":"Text"},{"id":"pmh:oai:pubmedcentral.nih.gov:6210996","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/6210996","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sensors (Basel)","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/s18103521","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s18103521","pdf_url":null,"source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2488890694","display_name":null,"funder_award_id":"61673160","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6236855828","display_name":null,"funder_award_id":"61751304","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7557561840","display_name":null,"funder_award_id":"U1604158","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8987167353","display_name":null,"funder_award_id":"U1509203","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":false,"pdf":false},"content_urls":null,"referenced_works_count":53,"referenced_works":["https://openalex.org/W1575494197","https://openalex.org/W1963557245","https://openalex.org/W1973889556","https://openalex.org/W1989006121","https://openalex.org/W1992558361","https://openalex.org/W1999053292","https://openalex.org/W2028328787","https://openalex.org/W2028586143","https://openalex.org/W2030049536","https://openalex.org/W2051463173","https://openalex.org/W2071124013","https://openalex.org/W2072105624","https://openalex.org/W2083520984","https://openalex.org/W2100495367","https://openalex.org/W2122593896","https://openalex.org/W2162399951","https://openalex.org/W2164207156","https://openalex.org/W2184192902","https://openalex.org/W2195459533","https://openalex.org/W2219903032","https://openalex.org/W2277064738","https://openalex.org/W2351763776","https://openalex.org/W2353978787","https://openalex.org/W2356671626","https://openalex.org/W2364921927","https://openalex.org/W2370839163","https://openalex.org/W2387959580","https://openalex.org/W2423656989","https://openalex.org/W2440930599","https://openalex.org/W2480364715","https://openalex.org/W2589171657","https://openalex.org/W2591591405","https://openalex.org/W2595796352","https://openalex.org/W2603334767","https://openalex.org/W2605673312","https://openalex.org/W2730803947","https://openalex.org/W2741289421","https://openalex.org/W2746699601","https://openalex.org/W2762355244","https://openalex.org/W2765284480","https://openalex.org/W2765296483","https://openalex.org/W2768753204","https://openalex.org/W2771734292","https://openalex.org/W2788508206","https://openalex.org/W2795516651","https://openalex.org/W2802357418","https://openalex.org/W2807561979","https://openalex.org/W2808286220","https://openalex.org/W2919115771","https://openalex.org/W6668811813","https://openalex.org/W6742885259","https://openalex.org/W6760385162","https://openalex.org/W7035914032"],"related_works":["https://openalex.org/W3047363187","https://openalex.org/W3119773509","https://openalex.org/W3177373753","https://openalex.org/W2782295999","https://openalex.org/W2162306796","https://openalex.org/W1970292246","https://openalex.org/W2016162169","https://openalex.org/W4247952185","https://openalex.org/W1895367623","https://openalex.org/W1642462315"],"abstract_inverted_index":{"Rotating":[0],"machinery":[1,92],"usually":[2],"suffers":[3],"from":[4,184],"a":[5,36,56,77,138,146,179,193],"type":[6,33],"of":[7,34,90,104,173,196,205],"fault,":[8,35],"where":[9],"the":[10,15,21,26,44,64,101,109,118,121,125,153,159,164,171,174,185,197,202,206],"fault":[11,22,39,59,187,199],"feature":[12,23,79,140,149,156,167],"extracted":[13,24,130],"in":[14,25,43,63,108,158,209],"frequency":[16,45,103,155],"domain":[17,28,46,66,111,161],"is":[18,29,93,142,168],"significant,":[19],"while":[20],"time":[27,65,110,160],"insignificant.":[30],"For":[31],"this":[32,75,210],"deep":[37,57,81,134],"learning-based":[38,58],"diagnosis":[40,60,69,72,89,182,200],"method":[41,61,83,97,207],"developed":[42,62,143],"can":[47,98,151],"reach":[48],"high":[49,203],"accuracy":[50],"performance":[51],"without":[52],"real-time":[53,68,87,180],"performance,":[54],"whereas":[55],"obtains":[67],"with":[70],"lower":[71],"accuracy.":[73],"In":[74],"paper,":[76],"multimodal":[78,114,139],"fusion-based":[80],"learning":[82],"for":[84],"accurate":[85],"and":[86,124,192],"online":[88,181],"rotating":[91],"proposed.":[94],"The":[95],"proposed":[96,208],"directly":[99],"extract":[100],"potential":[102,154],"abnormal":[105],"features":[106,115],"involved":[107,157],"data.":[112,162,188],"Firstly,":[113],"corresponding":[116],"to":[117,144,177],"original":[119],"data,":[120,123],"slope":[122],"curvature":[126],"data":[127],"are":[128],"firstly":[129],"by":[131],"three":[132],"separate":[133],"neural":[135],"networks.":[136],"Then,":[137],"fusion":[141],"obtain":[145],"new":[147,166],"fused":[148,165],"that":[150],"characterize":[152],"Lastly,":[163],"used":[169],"as":[170],"input":[172],"Softmax":[175],"classifier":[176],"achieve":[178],"result":[183],"frequency-type":[186],"A":[189],"simulation":[190],"experiment":[191],"case":[194],"study":[195],"bearing":[198],"confirm":[201],"efficiency":[204],"paper.":[211]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":4}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}