{"id":"https://openalex.org/W4200334638","doi":"https://doi.org/10.1109/iccad52417.2021.9638761","title":"Simulation-trained AI-System for Two-Stage Fault Detection and Diagnosis of Rolling Bearings in Industrial Applications","display_name":"Simulation-trained AI-System for Two-Stage Fault Detection and Diagnosis of Rolling Bearings in Industrial Applications","publication_year":2021,"publication_date":"2021-11-03","ids":{"openalex":"https://openalex.org/W4200334638","doi":"https://doi.org/10.1109/iccad52417.2021.9638761"},"language":"en","primary_location":{"id":"doi:10.1109/iccad52417.2021.9638761","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccad52417.2021.9638761","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Conference on Control, Automation and Diagnosis (ICCAD)","raw_type":"proceedings-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":null,"display_name":"Dominic Brown","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dominic Brown","raw_affiliation_strings":["Institut f\u00fcr Kommunikationssysteme und Technologien, Wolfenb\u00fcttel, Gemany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institut f\u00fcr Kommunikationssysteme und Technologien, Wolfenb\u00fcttel, Gemany","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049106700","display_name":"Jannis Fritz","orcid":null},"institutions":[{"id":"https://openalex.org/I2799859273","display_name":"Ostfalia University of Applied Sciences","ror":"https://ror.org/01bk10867","country_code":"DE","type":"education","lineage":["https://openalex.org/I2799859273"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Jannis Fritz","raw_affiliation_strings":["Institute for Mechatronics Faculty of Mechanical Engineering, Ostfalia University of Applied Sciences, Wolfenb\u00fcttel, Gemany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute for Mechatronics Faculty of Mechanical Engineering, Ostfalia University of Applied Sciences, Wolfenb\u00fcttel, Gemany","institution_ids":["https://openalex.org/I2799859273"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5038479733","display_name":"Martin Strube","orcid":null},"institutions":[{"id":"https://openalex.org/I2799859273","display_name":"Ostfalia University of Applied Sciences","ror":"https://ror.org/01bk10867","country_code":"DE","type":"education","lineage":["https://openalex.org/I2799859273"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Martin Strube","raw_affiliation_strings":["Institute for Mechatronics Faculty of Mechanical Engineering, Ostfalia University of Applied Sciences, Wolfenb\u00fcttel, Gemany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute for Mechatronics Faculty of Mechanical Engineering, Ostfalia University of Applied Sciences, Wolfenb\u00fcttel, Gemany","institution_ids":["https://openalex.org/I2799859273"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.19729421,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"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/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.9983999729156494,"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":0.9983999729156494,"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/T11062","display_name":"Gear and Bearing Dynamics Analysis","score":0.9973000288009644,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical 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":0.9603999853134155,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/bearing","display_name":"Bearing (navigation)","score":0.7739396691322327},{"id":"https://openalex.org/keywords/superposition-principle","display_name":"Superposition principle","score":0.6893249750137329},{"id":"https://openalex.org/keywords/fault","display_name":"Fault (geology)","score":0.6564608812332153},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6120612621307373},{"id":"https://openalex.org/keywords/fault-detection-and-isolation","display_name":"Fault detection and isolation","score":0.5229345560073853},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.492450088262558},{"id":"https://openalex.org/keywords/fault-tree-analysis","display_name":"Fault tree analysis","score":0.45884761214256287},{"id":"https://openalex.org/keywords/k-nearest-neighbors-algorithm","display_name":"k-nearest neighbors algorithm","score":0.45102638006210327},{"id":"https://openalex.org/keywords/threshold-limit-value","display_name":"Threshold limit value","score":0.44933977723121643},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.4435109794139862},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4245561957359314},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4240378737449646},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.4162139892578125},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.36850178241729736},{"id":"https://openalex.org/keywords/reliability-engineering","display_name":"Reliability engineering","score":0.25944948196411133},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.25725215673446655},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.12393784523010254}],"concepts":[{"id":"https://openalex.org/C199978012","wikidata":"https://www.wikidata.org/wiki/Q1273815","display_name":"Bearing (navigation)","level":2,"score":0.7739396691322327},{"id":"https://openalex.org/C27753989","wikidata":"https://www.wikidata.org/wiki/Q284885","display_name":"Superposition principle","level":2,"score":0.6893249750137329},{"id":"https://openalex.org/C175551986","wikidata":"https://www.wikidata.org/wiki/Q47089","display_name":"Fault (geology)","level":2,"score":0.6564608812332153},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6120612621307373},{"id":"https://openalex.org/C152745839","wikidata":"https://www.wikidata.org/wiki/Q5438153","display_name":"Fault detection and isolation","level":3,"score":0.5229345560073853},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.492450088262558},{"id":"https://openalex.org/C107094494","wikidata":"https://www.wikidata.org/wiki/Q428453","display_name":"Fault tree analysis","level":2,"score":0.45884761214256287},{"id":"https://openalex.org/C113238511","wikidata":"https://www.wikidata.org/wiki/Q1071612","display_name":"k-nearest neighbors algorithm","level":2,"score":0.45102638006210327},{"id":"https://openalex.org/C64413873","wikidata":"https://www.wikidata.org/wiki/Q21005","display_name":"Threshold limit value","level":2,"score":0.44933977723121643},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.4435109794139862},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4245561957359314},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4240378737449646},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.4162139892578125},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.36850178241729736},{"id":"https://openalex.org/C200601418","wikidata":"https://www.wikidata.org/wiki/Q2193887","display_name":"Reliability engineering","level":1,"score":0.25944948196411133},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.25725215673446655},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.12393784523010254},{"id":"https://openalex.org/C172707124","wikidata":"https://www.wikidata.org/wiki/Q423488","display_name":"Actuator","level":2,"score":0.0},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0},{"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/C99454951","wikidata":"https://www.wikidata.org/wiki/Q932068","display_name":"Environmental health","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":1,"locations":[{"id":"doi:10.1109/iccad52417.2021.9638761","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccad52417.2021.9638761","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Conference on Control, Automation and Diagnosis (ICCAD)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5799999833106995,"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W243674440","https://openalex.org/W1964511482","https://openalex.org/W1974543974","https://openalex.org/W2006347217","https://openalex.org/W2019505419","https://openalex.org/W2061698056","https://openalex.org/W2067423267","https://openalex.org/W2119530413","https://openalex.org/W2122646361","https://openalex.org/W2128734408","https://openalex.org/W2138000170","https://openalex.org/W2139833307","https://openalex.org/W2230524333","https://openalex.org/W2296077894","https://openalex.org/W2488793338","https://openalex.org/W2568782760","https://openalex.org/W2773210308","https://openalex.org/W2888728157","https://openalex.org/W3091996012","https://openalex.org/W3108351918","https://openalex.org/W3117297357","https://openalex.org/W3127489621","https://openalex.org/W3135501047","https://openalex.org/W3138709573","https://openalex.org/W6730267373"],"related_works":["https://openalex.org/W4308749380","https://openalex.org/W2372481764","https://openalex.org/W2610947892","https://openalex.org/W1977264433","https://openalex.org/W2746368218","https://openalex.org/W4308448008","https://openalex.org/W2037841693","https://openalex.org/W2347371661","https://openalex.org/W2012943989","https://openalex.org/W2287286086"],"abstract_inverted_index":{"Rolling":[0],"bearings":[1,42],"are":[2,183],"an":[3,50,53,98,157],"essential":[4],"component":[5],"in":[6,21,27,68,156],"manufacturing":[7],"equipment":[8],"and":[9,40,63,109,139,160,172,217],"can":[10,64],"lead":[11],"to":[12,187],"production":[13],"losses":[14],"if":[15],"a":[16,36,59,90,103,112,128,144,193],"failure":[17],"is":[18,31,43,56,77,80,100,118,137,146,149,154,164,197,208],"not":[19,32,45],"detected":[20,180],"time.":[22],"Furthermore,":[23],"the":[24,83,93,96,124,132,140,152,167,173,176,179,188,201,204,218,221,224],"data":[25,94,122,163,202],"obtained":[26],"many":[28],"industrial":[29,69,158],"applications":[30],"labelled,":[33],"so":[34],"that":[35,55],"classification":[37],"of":[38,85,89,134,190,223],"intact":[39],"defective":[41],"often":[44],"possible.":[46],"This":[47,206],"paper":[48],"presents":[49],"approach":[51,105,207],"for":[52,73,106,131,170],"AI-system":[54,99,153],"trained":[57,199],"with":[58,102,166,185,200,210],"bearing":[60,74,215],"fault":[61,75,87,107,191],"simulation":[62,76],"then":[65],"be":[66],"deployed":[67,155],"applications.":[70],"A":[71],"method":[72],"presented":[78,225],"which":[79,143,196],"based":[81],"on":[82],"superposition":[84],"characteristic":[86],"frequencies":[88],"bearing.":[91],"With":[92],"from":[95,123,203],"simulation,":[97],"implemented":[101],"two-stage":[104],"detection":[108],"diagnosis.":[110],"First,":[111],"semi-supervised":[113],"k-nearest":[114],"neighbor":[115],"distance":[116],"measure":[117],"calculated":[119],"using":[120,192],"synthetic":[121],"simulation.":[125,205],"From":[126],"this,":[127],"suitable":[129],"value":[130,169],"number":[133],"neighbors":[135],"k":[136,171],"defined":[138],"threshold":[141],"at":[142],"sample":[145],"considered":[147],"faulty":[148,181],"determined.":[150],"Then":[151],"application":[159],"real":[161,213],"world":[162,214],"analysed":[165],"given":[168],"threshold.":[174],"In":[175],"second":[177],"stage,":[178],"samples":[182],"classified":[184],"regard":[186],"type":[189],"decision":[194],"tree,":[195],"also":[198],"validated":[209],"two":[211],"different":[212],"datasets":[216],"results":[219],"show":[220],"effectiveness":[222],"approach.":[226]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}