{"id":"https://openalex.org/W3211845688","doi":"https://doi.org/10.1109/iecon48115.2021.9589074","title":"Investigations on Numerical Techniques for Detecting Variations in Acoustic Emissions","display_name":"Investigations on Numerical Techniques for Detecting Variations in Acoustic Emissions","publication_year":2021,"publication_date":"2021-10-13","ids":{"openalex":"https://openalex.org/W3211845688","doi":"https://doi.org/10.1109/iecon48115.2021.9589074","mag":"3211845688"},"language":"en","primary_location":{"id":"doi:10.1109/iecon48115.2021.9589074","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iecon48115.2021.9589074","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IECON 2021 \u2013 47th Annual Conference of the IEEE Industrial Electronics Society","raw_type":"proceedings-article"},"type":"conference-paper","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/A5036343536","display_name":"Selvine G. Mathias","orcid":"https://orcid.org/0000-0002-6549-0763"},"institutions":[{"id":"https://openalex.org/I4210106192","display_name":"Technische Hochschule Ingolstadt","ror":"https://ror.org/02bxzcy64","country_code":"DE","type":"education","lineage":["https://openalex.org/I4210106192"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Selvine G. Mathias","raw_affiliation_strings":["Technische Hochschule Ingolstadt, Ingolstadt, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Technische Hochschule Ingolstadt, Ingolstadt, Germany","institution_ids":["https://openalex.org/I4210106192"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068407097","display_name":"Mathew John Mancha","orcid":"https://orcid.org/0009-0006-0932-9779"},"institutions":[{"id":"https://openalex.org/I1322300227","display_name":"Audi (Germany)","ror":"https://ror.org/02aykj333","country_code":"DE","type":"company","lineage":["https://openalex.org/I1322300227"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Mathew John Mancha","raw_affiliation_strings":["Technologieentwicklung Gu\u00df/Profil, AUDI AG, Ingolstadt, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Technologieentwicklung Gu\u00df/Profil, AUDI AG, Ingolstadt, Germany","institution_ids":["https://openalex.org/I1322300227"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5106615617","display_name":"Daniel Gro\u00dfmann","orcid":"https://orcid.org/0000-0002-7388-5757"},"institutions":[{"id":"https://openalex.org/I4210106192","display_name":"Technische Hochschule Ingolstadt","ror":"https://ror.org/02bxzcy64","country_code":"DE","type":"education","lineage":["https://openalex.org/I4210106192"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Daniel Grossmann","raw_affiliation_strings":["Technische Hochschule Ingolstadt, Ingolstadt, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Technische Hochschule Ingolstadt, Ingolstadt, Germany","institution_ids":["https://openalex.org/I4210106192"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012723024","display_name":"Bernd Kujat","orcid":null},"institutions":[{"id":"https://openalex.org/I1322300227","display_name":"Audi (Germany)","ror":"https://ror.org/02aykj333","country_code":"DE","type":"company","lineage":["https://openalex.org/I1322300227"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Bernd Kujat","raw_affiliation_strings":["Technologieentwicklung Gu\u00df/Profil, AUDI AG, Ingolstadt, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Technologieentwicklung Gu\u00df/Profil, AUDI AG, Ingolstadt, Germany","institution_ids":["https://openalex.org/I1322300227"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5061613500","display_name":"Kay Schiebold","orcid":null},"institutions":[{"id":"https://openalex.org/I1322300227","display_name":"Audi (Germany)","ror":"https://ror.org/02aykj333","country_code":"DE","type":"company","lineage":["https://openalex.org/I1322300227"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Kay Schiebold","raw_affiliation_strings":["Technologieentwicklung Gu\u00df/Profil, AUDI AG, Ingolstadt, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Technologieentwicklung Gu\u00df/Profil, AUDI AG, Ingolstadt, Germany","institution_ids":["https://openalex.org/I1322300227"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.1465,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.42079712,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9927999973297119,"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"}},"topics":[{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9927999973297119,"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/T10876","display_name":"Fault Detection and Control Systems","score":0.9905999898910522,"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/T10534","display_name":"Structural Health Monitoring Techniques","score":0.9890000224113464,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural 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/cluster-analysis","display_name":"Cluster analysis","score":0.81120765209198},{"id":"https://openalex.org/keywords/acoustic-emission","display_name":"Acoustic emission","score":0.6348318457603455},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6015059947967529},{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.5712521076202393},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.5417623519897461},{"id":"https://openalex.org/keywords/cluster","display_name":"Cluster (spacecraft)","score":0.5084359049797058},{"id":"https://openalex.org/keywords/acoustics","display_name":"Acoustics","score":0.4902108311653137},{"id":"https://openalex.org/keywords/signal-processing","display_name":"Signal processing","score":0.4720388650894165},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.4330708980560303},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4203128218650818},{"id":"https://openalex.org/keywords/time-domain","display_name":"Time domain","score":0.4163546562194824},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.41254380345344543},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.36703163385391235},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.25758522748947144},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.20691874623298645},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.13914638757705688},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.09927251935005188}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.81120765209198},{"id":"https://openalex.org/C174598085","wikidata":"https://www.wikidata.org/wiki/Q746673","display_name":"Acoustic emission","level":2,"score":0.6348318457603455},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6015059947967529},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.5712521076202393},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.5417623519897461},{"id":"https://openalex.org/C164866538","wikidata":"https://www.wikidata.org/wiki/Q367351","display_name":"Cluster (spacecraft)","level":2,"score":0.5084359049797058},{"id":"https://openalex.org/C24890656","wikidata":"https://www.wikidata.org/wiki/Q82811","display_name":"Acoustics","level":1,"score":0.4902108311653137},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.4720388650894165},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.4330708980560303},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4203128218650818},{"id":"https://openalex.org/C103824480","wikidata":"https://www.wikidata.org/wiki/Q185889","display_name":"Time domain","level":2,"score":0.4163546562194824},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.41254380345344543},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.36703163385391235},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.25758522748947144},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.20691874623298645},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.13914638757705688},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.09927251935005188},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iecon48115.2021.9589074","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iecon48115.2021.9589074","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IECON 2021 \u2013 47th Annual Conference of the IEEE Industrial Electronics Society","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.4699999988079071}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":8,"referenced_works":["https://openalex.org/W649559325","https://openalex.org/W1545004066","https://openalex.org/W2032386854","https://openalex.org/W2801529761","https://openalex.org/W2894787954","https://openalex.org/W2947675090","https://openalex.org/W2963372895","https://openalex.org/W6766016268"],"related_works":["https://openalex.org/W2393097294","https://openalex.org/W2889442519","https://openalex.org/W2381188978","https://openalex.org/W2364223432","https://openalex.org/W2988080746","https://openalex.org/W2377260462","https://openalex.org/W2158646189","https://openalex.org/W2472814183","https://openalex.org/W1992295166","https://openalex.org/W2143508933"],"abstract_inverted_index":{"The":[0,113],"objective":[1],"of":[2,11,21,38,74,80],"this":[3],"paper":[4,54],"is":[5,41],"to":[6,42,62,107,147],"present":[7],"a":[8,83,129],"hybrid":[9],"methodology":[10],"analysing":[12],"acoustic":[13,69],"signals":[14,104,120,145],"arising":[15],"in":[16,50,71],"industrial":[17],"processes":[18],"through":[19],"comparisons":[20],"known":[22],"numerical":[23],"techniques":[24],"such":[25,91],"as":[26,92],"clustering.":[27],"Apart":[28],"from":[29,68],"data":[30],"acquisition":[31],"and":[32,59,77,101,136,141],"pre-processing,":[33],"the":[34,72,110,144,148],"other":[35],"essential":[36],"component":[37],"using":[39],"acoustics":[40],"design":[43],"an":[44,86],"analysis":[45],"methodology,":[46],"that":[47,116,121],"can":[48,133],"culminate":[49],"practical":[51],"applications.":[52],"This":[53],"applies":[55],"Gaussian":[56],"Mixture":[57],"Models":[58],"Self-Organising":[60],"Maps":[61],"cluster":[63],"pre-processed":[64],"AE":[65,119],"hits":[66],"obtained":[67,123],"sensors":[70],"form":[73],"tensile,":[75],"shear":[76],"mixed":[78],"modes":[79],"compression":[81],"on":[82],"material.":[84],"For":[85],"in-depth":[87],"analysis,":[88],"custom":[89],"features":[90],"high":[93],"peak":[94,97],"regions,":[95,98],"low":[96],"strongly":[99],"hit":[100,103],"weakly":[102],"are":[105,122],"introduced":[106],"compare":[108],"with":[109],"clusters":[111],"formed.":[112],"results":[114],"show":[115],"for":[117,138],"small":[118],"or":[124],"extracted":[125],"after":[126],"events":[127],"detection,":[128],"time-domain":[130],"based":[131],"clustering":[132],"be":[134],"applied":[135],"used":[137],"isolating":[139],"similarities":[140],"distinctions":[142],"among":[143],"belonging":[146],"same":[149],"group.":[150]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
