{"id":"https://openalex.org/W4403447305","doi":"https://doi.org/10.1109/etfa61755.2024.10711048","title":"Encoding Machine Phase Information into Heterogeneous Graphs for Adaptive Fault Diagnosis","display_name":"Encoding Machine Phase Information into Heterogeneous Graphs for Adaptive Fault Diagnosis","publication_year":2024,"publication_date":"2024-09-10","ids":{"openalex":"https://openalex.org/W4403447305","doi":"https://doi.org/10.1109/etfa61755.2024.10711048"},"language":"en","primary_location":{"id":"doi:10.1109/etfa61755.2024.10711048","is_oa":false,"landing_page_url":"https://doi.org/10.1109/etfa61755.2024.10711048","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE 29th International Conference on Emerging Technologies and Factory Automation (ETFA)","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":"https://openalex.org/A5016714584","display_name":"Maximilian-Peter Radtke","orcid":null},"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":true,"raw_author_name":"Maximilian-Peter Radtke","raw_affiliation_strings":["Technische Hochschule Ingolstadt,Ingolstadt,Germany"],"affiliations":[{"raw_affiliation_string":"Technische Hochschule Ingolstadt,Ingolstadt,Germany","institution_ids":["https://openalex.org/I4210106192"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031354877","display_name":"Marco F. Huber","orcid":"https://orcid.org/0000-0002-8250-2092"},"institutions":[{"id":"https://openalex.org/I100066346","display_name":"University of Stuttgart","ror":"https://ror.org/04vnq7t77","country_code":"DE","type":"education","lineage":["https://openalex.org/I100066346"]},{"id":"https://openalex.org/I3018473509","display_name":"Fraunhofer Institute for Manufacturing Engineering and Automation","ror":"https://ror.org/01rvqha10","country_code":"DE","type":"facility","lineage":["https://openalex.org/I3018473509","https://openalex.org/I4923324"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Marco F. Huber","raw_affiliation_strings":["University of Stuttgart and Fraunhofer IPA,Stuttgart,Germany"],"affiliations":[{"raw_affiliation_string":"University of Stuttgart and Fraunhofer IPA,Stuttgart,Germany","institution_ids":["https://openalex.org/I3018473509","https://openalex.org/I100066346"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5041389715","display_name":"J\u00fcrgen Bock","orcid":"https://orcid.org/0000-0002-1210-1576"},"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":"J\u00fcrgen Bock","raw_affiliation_strings":["Technische Hochschule Ingolstadt,Ingolstadt,Germany"],"affiliations":[{"raw_affiliation_string":"Technische Hochschule Ingolstadt,Ingolstadt,Germany","institution_ids":["https://openalex.org/I4210106192"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5016714584"],"corresponding_institution_ids":["https://openalex.org/I4210106192"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.21111572,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10876","display_name":"Fault Detection and Control Systems","score":0.9437000155448914,"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/T10876","display_name":"Fault Detection and Control Systems","score":0.9437000155448914,"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/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.9433000087738037,"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/T13293","display_name":"Engineering and Test Systems","score":0.9023000001907349,"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/computer-science","display_name":"Computer science","score":0.7107588648796082},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.6471211910247803},{"id":"https://openalex.org/keywords/phase","display_name":"Phase (matter)","score":0.5356845855712891},{"id":"https://openalex.org/keywords/fault","display_name":"Fault (geology)","score":0.5155020356178284},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.28764569759368896}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7107588648796082},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.6471211910247803},{"id":"https://openalex.org/C44280652","wikidata":"https://www.wikidata.org/wiki/Q104837","display_name":"Phase (matter)","level":2,"score":0.5356845855712891},{"id":"https://openalex.org/C175551986","wikidata":"https://www.wikidata.org/wiki/Q47089","display_name":"Fault (geology)","level":2,"score":0.5155020356178284},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.28764569759368896},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"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/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/etfa61755.2024.10711048","is_oa":false,"landing_page_url":"https://doi.org/10.1109/etfa61755.2024.10711048","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE 29th International Conference on Emerging Technologies and Factory Automation (ETFA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Climate action","score":0.8100000023841858,"id":"https://metadata.un.org/sdg/13"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W1677182931","https://openalex.org/W1731081199","https://openalex.org/W2164943005","https://openalex.org/W2892035503","https://openalex.org/W3022112205","https://openalex.org/W3087257704","https://openalex.org/W3214190848","https://openalex.org/W4200312990","https://openalex.org/W4200473862","https://openalex.org/W4283800330","https://openalex.org/W4293258577","https://openalex.org/W4362506827","https://openalex.org/W4385163582","https://openalex.org/W4388115963","https://openalex.org/W4389570778","https://openalex.org/W4391328318","https://openalex.org/W4392246861","https://openalex.org/W4394896965","https://openalex.org/W6797464607","https://openalex.org/W6811425011"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052","https://openalex.org/W4402327032"],"abstract_inverted_index":{"Machinery":[0],"fault":[1,29,120],"diagnosis":[2,121],"is":[3],"increasingly":[4],"reliant":[5],"on":[6],"data-driven":[7],"algorithms,":[8],"yet":[9],"struggles":[10],"with":[11,31,69,79],"adapting":[12],"to":[13,76,91],"unseen":[14],"operating":[15],"conditions.":[16],"To":[17],"address":[18],"this,":[19],"we":[20],"propose":[21],"integrating":[22],"phase":[23],"information":[24],"into":[25,112],"heterogeneous":[26],"graphs":[27],"for":[28,93],"diagnostics":[30],"Graph":[32],"Neural":[33,84],"Networks":[34,85],"(GNNs).":[35],"Our":[36],"method":[37],"involves":[38],"identifying":[39],"the":[40,52,88,105,114],"distinct":[41],"phases":[42],"that":[43],"a":[44,48,62,80,100],"machine":[45],"undergoes":[46],"within":[47],"cycle":[49],"and":[50,72,116],"segmenting":[51],"signals":[53,57],"accordingly.":[54],"These":[55],"segmented":[56],"are":[58],"then":[59],"represented":[60],"in":[61,99],"graph":[63,77],"of":[64,107,119],"multiple":[65],"connected":[66],"sensor":[67],"networks":[68],"diverse":[70],"node":[71,89],"edge":[73],"types.":[74],"Prior":[75],"classification":[78],"GNN,":[81],"individual":[82],"Convolutional":[83],"(CNNs)":[86],"preprocess":[87],"attributes":[90],"account":[92],"their":[94],"unique":[95],"characteristics.":[96],"The":[97],"evaluation":[98],"domain":[101,117],"adaptation":[102],"setting":[103],"demonstrates":[104],"effectiveness":[106],"our":[108],"approach,":[109],"offering":[110],"insights":[111],"improving":[113],"robustness":[115],"adaptability":[118],"models.":[122]},"counts_by_year":[],"updated_date":"2025-12-27T23:08:20.325037","created_date":"2025-10-10T00:00:00"}