{"id":"https://openalex.org/W4210287574","doi":"https://doi.org/10.1109/safeprocess52771.2021.9693630","title":"GCN-CAM: A new graph convolutional network-based fault diagnosis method with its interpretability analysis","display_name":"GCN-CAM: A new graph convolutional network-based fault diagnosis method with its interpretability analysis","publication_year":2021,"publication_date":"2021-12-17","ids":{"openalex":"https://openalex.org/W4210287574","doi":"https://doi.org/10.1109/safeprocess52771.2021.9693630"},"language":"en","primary_location":{"id":"doi:10.1109/safeprocess52771.2021.9693630","is_oa":false,"landing_page_url":"https://doi.org/10.1109/safeprocess52771.2021.9693630","pdf_url":null,"source":{"id":"https://openalex.org/S4363605570","display_name":"2021 CAA Symposium on Fault Detection, Supervision, and Safety for Technical Processes (SAFEPROCESS)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 CAA Symposium on Fault Detection, Supervision, and Safety for Technical Processes (SAFEPROCESS)","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/A5100741331","display_name":"Zhiwen Chen","orcid":"https://orcid.org/0000-0002-4759-0904"},"institutions":[{"id":"https://openalex.org/I139660479","display_name":"Central South University","ror":"https://ror.org/00f1zfq44","country_code":"CN","type":"education","lineage":["https://openalex.org/I139660479"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhiwen Chen","raw_affiliation_strings":["School of Automation, Central South University, Changsha, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Automation, Central South University, Changsha, China","institution_ids":["https://openalex.org/I139660479"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081489097","display_name":"Jiamin Xu","orcid":"https://orcid.org/0000-0001-6749-1260"},"institutions":[{"id":"https://openalex.org/I4210136793","display_name":"Peng Cheng Laboratory","ror":"https://ror.org/03qdqbt06","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210136793"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiamin Xu","raw_affiliation_strings":["Pengcheng laboratory, Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Pengcheng laboratory, Shenzhen, China","institution_ids":["https://openalex.org/I4210136793"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026259132","display_name":"Tao Peng","orcid":"https://orcid.org/0000-0002-7662-0471"},"institutions":[{"id":"https://openalex.org/I4210136793","display_name":"Peng Cheng Laboratory","ror":"https://ror.org/03qdqbt06","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210136793"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tao Peng","raw_affiliation_strings":["Pengcheng laboratory, Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Pengcheng laboratory, Shenzhen, China","institution_ids":["https://openalex.org/I4210136793"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101971818","display_name":"Chunhua Yang","orcid":"https://orcid.org/0000-0002-3770-9887"},"institutions":[{"id":"https://openalex.org/I4210136793","display_name":"Peng Cheng Laboratory","ror":"https://ror.org/03qdqbt06","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210136793"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chunhua Yang","raw_affiliation_strings":["Pengcheng laboratory, Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Pengcheng laboratory, Shenzhen, China","institution_ids":["https://openalex.org/I4210136793"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101465303","display_name":"Xinyu Fan","orcid":"https://orcid.org/0000-0001-7514-6766"},"institutions":[{"id":"https://openalex.org/I4210136793","display_name":"Peng Cheng Laboratory","ror":"https://ror.org/03qdqbt06","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210136793"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinyu Fan","raw_affiliation_strings":["Pengcheng laboratory, Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Pengcheng laboratory, Shenzhen, China","institution_ids":["https://openalex.org/I4210136793"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100711520","display_name":"Weihua Gui","orcid":"https://orcid.org/0000-0002-5337-6445"},"institutions":[{"id":"https://openalex.org/I4210136793","display_name":"Peng Cheng Laboratory","ror":"https://ror.org/03qdqbt06","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210136793"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weihua Gui","raw_affiliation_strings":["Pengcheng laboratory, Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Pengcheng laboratory, Shenzhen, China","institution_ids":["https://openalex.org/I4210136793"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.221,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.78664653,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"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/T10409","display_name":"Fuel Cells and Related Materials","score":0.9940999746322632,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/T10409","display_name":"Fuel Cells and Related Materials","score":0.9940999746322632,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/T11948","display_name":"Machine Learning in Materials Science","score":0.9890999794006348,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12676","display_name":"Machine Learning and ELM","score":0.9751999974250793,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/interpretability","display_name":"Interpretability","score":0.9284217357635498},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6029635071754456},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5409035086631775},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5402348637580872},{"id":"https://openalex.org/keywords/softmax-function","display_name":"Softmax function","score":0.5369583964347839},{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.49796485900878906},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4102633595466614},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3804601728916168},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3570520281791687},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.27919530868530273}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.9284217357635498},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6029635071754456},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5409035086631775},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5402348637580872},{"id":"https://openalex.org/C188441871","wikidata":"https://www.wikidata.org/wiki/Q7554146","display_name":"Softmax function","level":3,"score":0.5369583964347839},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.49796485900878906},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4102633595466614},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3804601728916168},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3570520281791687},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.27919530868530273}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/safeprocess52771.2021.9693630","is_oa":false,"landing_page_url":"https://doi.org/10.1109/safeprocess52771.2021.9693630","pdf_url":null,"source":{"id":"https://openalex.org/S4363605570","display_name":"2021 CAA Symposium on Fault Detection, Supervision, and Safety for Technical Processes (SAFEPROCESS)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 CAA Symposium on Fault Detection, Supervision, and Safety for Technical Processes (SAFEPROCESS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320322843","display_name":"Natural Science Foundation of\u00a0Hunan Province","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W2102605133","https://openalex.org/W2137175628","https://openalex.org/W2590252915","https://openalex.org/W2610961739","https://openalex.org/W2804879845","https://openalex.org/W2907492528","https://openalex.org/W2964015378","https://openalex.org/W2972317931","https://openalex.org/W2979481854","https://openalex.org/W3094594436","https://openalex.org/W3113649798","https://openalex.org/W3135695944","https://openalex.org/W3181971244","https://openalex.org/W6675026286","https://openalex.org/W6726873649","https://openalex.org/W6767288045","https://openalex.org/W6798193057"],"related_works":["https://openalex.org/W2905433371","https://openalex.org/W3107204728","https://openalex.org/W4287591324","https://openalex.org/W3108503355","https://openalex.org/W3090555870","https://openalex.org/W2888392564","https://openalex.org/W3134502938","https://openalex.org/W4401096132","https://openalex.org/W2899027234","https://openalex.org/W3015684221"],"abstract_inverted_index":{"Recently,":[0],"graph":[1,33],"neural":[2],"network":[3],"(GCN)-based":[4],"fault":[5,49,158],"diagnosis":[6,50,168],"methods":[7,19],"have":[8],"been":[9],"received":[10],"increasing":[11],"attention.":[12],"However,":[13],"regarding":[14],"the":[15,45,53,63,78,82,93,106,111,121,124,133,147,155,167],"obtained":[16],"results,":[17],"existing":[18],"are":[20,102,117],"often":[21],"lack":[22],"of":[23,47,55,115,123,132,135,157],"interpretability.":[24],"To":[25,119],"solve":[26],"this":[27,30],"problem,":[28],"in":[29,57],"paper,":[31],"a":[32,99,129,136,163],"convolutional":[34],"network-class":[35],"activation":[36],"mapping":[37],"(GCNCAM)":[38],"model":[39,64,150],"is":[40,59,139],"established,":[41],"which":[42],"can":[43,151],"interpret":[44],"results":[46,80,144,169],"GCN-based":[48],"method.":[51],"Specifically,":[52],"definition":[54],"interpretability":[56,113],"GCN":[58,66],"firstly":[60],"clarified,":[61],"then":[62],"uses":[65,75],"layer":[67,87,97,101],"to":[68,104],"extract":[69],"features":[70],"ZGCNof":[71],"input":[72],"measurements,":[73],"it":[74],"ZGCNto":[76],"obtain":[77],"diagnostic":[79],"through":[81],"global":[83],"average":[84],"pooling":[85],"(GAP)":[86],"and":[88,98,110],"fully":[89,95],"connected":[90,96],"layer.":[91],"Next,":[92],"trained":[94],"softmax":[100],"used":[103],"process":[105],"output":[107],"value":[108],"ZGCNdirectly,":[109],"normalized":[112],"vectors":[114],"measurements":[116],"obtained.":[118],"verify":[120],"effectiveness":[122],"method,":[125],"an":[126],"experiment":[127],"on":[128],"hardware-in-the-loop":[130],"platform":[131],"rectifier":[134],"high-speed":[137],"train":[138],"carried":[140],"out.":[141],"The":[142],"experimental":[143],"show":[145],"that":[146],"proposed":[148],"GCN-CAM":[149],"not":[152],"only":[153],"complete":[154],"task":[156],"diagnosis,":[159],"but":[160],"also":[161],"provide":[162],"convincing":[164],"explanation":[165],"for":[166]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
