{"id":"https://openalex.org/W3081266760","doi":null,"title":"Use of a deep convolutional neural network and beamforming for localisation and diagnosis of industry noise sources","display_name":"Use of a deep convolutional neural network and beamforming for localisation and diagnosis of industry noise sources","publication_year":2020,"publication_date":"2020-01-01","ids":{"openalex":"https://openalex.org/W3081266760","mag":"3081266760"},"language":"en","primary_location":{"id":"pmh:oai:pure.atira.dk:publications/e08eb6b5-7713-4533-a0b2-b0797896c13f","doi":null,"is_oa":false,"landing_page_url":"https://admin.research-repository.uwa.edu.au/en/publications/e08eb6b5-7713-4533-a0b2-b0797896c13f","pdf_url":null,"source":{"id":"https://openalex.org/S4306402492","display_name":"UWA Profiles and Research Repository (UWA)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I177877127","host_organization_name":"The University of Western Australia","host_organization_lineage":["https://openalex.org/I177877127"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"info:eu-repo/semantics/conferenceObject"},"volume":null,"type":"conference-paper","is_traditional":true,"indexed_in":[],"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/A5066461939","display_name":"G. Capon","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I177877127","display_name":"The University of Western Australia","ror":"https://ror.org/047272k79","country_code":"AU","type":"education","lineage":["https://openalex.org/I177877127"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Capon, G.","raw_affiliation_strings":["University of Western Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Western Australia","institution_ids":["https://openalex.org/I177877127"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000029156","display_name":"Hayden Chen","orcid":"https://orcid.org/0009-0009-7432-9005","observed_orcids":["https://orcid.org/0009-0009-7432-9005"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, H.","raw_affiliation_strings":["Electrical, Electronic and Computer Engineering"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Electrical, Electronic and Computer Engineering","institution_ids":[]}]},{"author_position":"middle","author":{"id":null,"display_name":"Bao, C.","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bao, C.","raw_affiliation_strings":["Mechanical Engineering"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Mechanical Engineering","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020406814","display_name":"Heyuan Sun","orcid":"https://orcid.org/0009-0001-6060-9526","observed_orcids":["https://orcid.org/0009-0001-6060-9526"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sun, H.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074051740","display_name":"D. Matthews","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Matthews, D.","raw_affiliation_strings":["Mechanical Engineering"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Mechanical Engineering","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101609099","display_name":"Jifei Pan","orcid":"https://orcid.org/0000-0002-9278-7060","observed_orcids":["https://orcid.org/0000-0002-9278-7060","https://orcid.org/0009-0001-7565-6026"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pan, J.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.3354,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.37939822,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":87,"max":95},"biblio":{"volume":null,"issue":null,"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":0.9945999979972839,"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":0.9945999979972839,"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/T10931","display_name":"Direction-of-Arrival Estimation Techniques","score":0.001500000013038516,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T11309","display_name":"Music and Audio Processing","score":0.0012000000569969416,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/fault-diagnosis","display_name":"fault diagnosis","score":0.7279999852180481},{"id":"https://openalex.org/keywords/condition-monitoring","display_name":"condition monitoring","score":0.7160000205039978},{"id":"https://openalex.org/keywords/adaptive-beamforming","display_name":"adaptive beamforming","score":0.8679999709129333},{"id":"https://openalex.org/keywords/deep-convolutional-neural-networks","display_name":"deep convolutional neural networks","score":0.7860000133514404},{"id":"https://openalex.org/keywords/spectrogram-classification","display_name":"spectrogram classification","score":0.8669999837875366},{"id":"https://openalex.org/keywords/microphone-array","display_name":"microphone array","score":0.843999981880188},{"id":"https://openalex.org/keywords/rotating-machinery","display_name":"rotating machinery","score":0.699999988079071},{"id":"https://openalex.org/keywords/acoustic-emission","display_name":"acoustic emission","score":0.43299999833106995},{"id":"https://openalex.org/keywords/damage-detection","display_name":"damage detection","score":0.640999972820282},{"id":"https://openalex.org/keywords/signal-separation","display_name":"signal separation","score":0.3409999907016754}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7257247567176819},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.6026365160942078},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5531222820281982},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5279532670974731},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5063959360122681},{"id":"https://openalex.org/C54197355","wikidata":"https://www.wikidata.org/wiki/Q5782992","display_name":"Beamforming","level":2,"score":0.48695099353790283},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.43087077140808105},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4020600914955139},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.32565250992774963},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.23829305171966553},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"pmh:oai:pure.atira.dk:publications/e08eb6b5-7713-4533-a0b2-b0797896c13f","doi":null,"is_oa":false,"landing_page_url":"https://admin.research-repository.uwa.edu.au/en/publications/e08eb6b5-7713-4533-a0b2-b0797896c13f","pdf_url":null,"source":{"id":"https://openalex.org/S4306402492","display_name":"UWA Profiles and Research Repository (UWA)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I177877127","host_organization_name":"The University of Western Australia","host_organization_lineage":["https://openalex.org/I177877127"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","score":0.7207000255584717,"id":"https://metadata.un.org/sdg/9"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":6,"referenced_works":["https://openalex.org/W653761051","https://openalex.org/W2061074721","https://openalex.org/W2163605009","https://openalex.org/W2805598672","https://openalex.org/W2809254203","https://openalex.org/W2949117887"],"related_works":["https://openalex.org/W2199901494","https://openalex.org/W3107700952","https://openalex.org/W7137974298","https://openalex.org/W4389363350","https://openalex.org/W2966393506","https://openalex.org/W7213968160","https://openalex.org/W4407403161","https://openalex.org/W3217004716","https://openalex.org/W4389228241","https://openalex.org/W4414220110"],"abstract_inverted_index":{"Many":[0],"industries":[1],"rely":[2],"on":[3,92,110,168],"the":[4,71,98,102,107,111,122,135,143],"continual":[5],"and":[6,16,20,27,31,40,60,79,141,175],"unimpeded":[7],"operation":[8],"of":[9,46,73,100,104,124,138,147,152],"turbines,":[10],"pumps,":[11],"pulleys,":[12],"fans,":[13],"motors,":[14],"gearboxes,":[15],"other":[17],"associated":[18],"fixed":[19],"mobile":[21],"plant":[22],"equipment.":[23],"As":[24],"such,":[25],"reliable":[26],"remote":[28],"condition":[29,47],"monitoring":[30,48,189],"fault":[32],"localisation":[33,78],"can":[34,55],"improve":[35],"safety,":[36],"prevent":[37],"unnecessary":[38],"downtime":[39],"reduce":[41],"maintenance":[42],"costs.":[43],"Current":[44],"methods":[45],"such":[49],"as":[50,128],"acoustic":[51],"emissions":[52],"(AE)":[53],"testing":[54],"prove":[56],"difficult":[57],"to":[58],"automate":[59],"require":[61],"careful":[62],"analysis":[63],"by":[64],"a":[65,85],"trained":[66],"analyst.":[67],"This":[68,116,180],"research":[69,154],"investigates":[70],"use":[72,123,185],"adaptive":[74],"beamforming":[75,173],"for":[76,131,184,191,197],"source":[77],"signal":[80,146],"extraction":[81],"in":[82,106,186],"conjunction":[83],"with":[84,121,134,194],"convolutional":[86],"neural":[87],"network":[88,113],"classification":[89,114],"system":[90,190],"based":[91,167],"spectrogram":[93],"plots.":[94],"Furthermore,":[95],"it":[96],"tests":[97],"effects":[99],"reducing":[101],"number":[103],"microphones":[105],"microphone":[108],"array":[109,176],"deep":[112],"accuracy.":[115],"technology":[117,181],"has":[118],"been":[119],"investigated":[120],"12-volt":[125],"computer":[126],"fans":[127],"an":[129,187],"analogue":[130],"rotating":[132],"machinery,":[133],"primary":[136],"challenge":[137],"reliably":[139],"separating":[140],"classifying":[142],"unique":[144],"spectral":[145],"each":[148],"fan.":[149],"The":[150],"outcome":[151],"this":[153],"from":[155],"over":[156],"450":[157],"test":[158],"samples":[159],"demonstrates":[160],"damage":[161],"detection":[162],"accuracy":[163],"consistently":[164],"above":[165],"97%":[166],"available":[169,195],"data":[170],"when":[171],"adequate":[172],"resolution":[174],"gain":[177],"are":[178],"achieved.":[179],"shows":[182],"promise":[183],"automated":[188],"industrial":[192],"applications,":[193],"scope":[196],"further":[198],"refinements.":[199]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2021,"cited_by_count":1}],"updated_date":"2026-11-06T12:05:46.655746","created_date":"2020-09-01T00:00:00"}