{"id":"https://openalex.org/W4404577274","doi":"https://doi.org/10.1109/icmlca63499.2024.10753882","title":"Fault Diagnosis of Suspension Controllers Based on KAN-ResNet","display_name":"Fault Diagnosis of Suspension Controllers Based on KAN-ResNet","publication_year":2024,"publication_date":"2024-10-18","ids":{"openalex":"https://openalex.org/W4404577274","doi":"https://doi.org/10.1109/icmlca63499.2024.10753882"},"language":"en","primary_location":{"id":"doi:10.1109/icmlca63499.2024.10753882","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmlca63499.2024.10753882","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 5th International Conference on Machine Learning and Computer Application (ICMLCA)","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/A5101477963","display_name":"Jingwen Zheng","orcid":"https://orcid.org/0000-0002-5734-8388"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jingwen Zheng","raw_affiliation_strings":["CRRC Qingdao Sifang Co., Ltd.,State Key Laboratory of High-speed Maglev Transportation Technology,Qingdao City,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"CRRC Qingdao Sifang Co., Ltd.,State Key Laboratory of High-speed Maglev Transportation Technology,Qingdao City,China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100425365","display_name":"Mingwei Li","orcid":"https://orcid.org/0000-0003-3193-3180"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mingwei Li","raw_affiliation_strings":["CRRC Qingdao Sifang Co., Ltd.,State Key Laboratory of High-speed Maglev Transportation Technology,Qingdao City,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"CRRC Qingdao Sifang Co., Ltd.,State Key Laboratory of High-speed Maglev Transportation Technology,Qingdao City,China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102339655","display_name":"Yi Tian","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yi Tian","raw_affiliation_strings":["CRRC Qingdao Sifang Co., Ltd.,State Key Laboratory of High-speed Maglev Transportation Technology,Qingdao City,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"CRRC Qingdao Sifang Co., Ltd.,State Key Laboratory of High-speed Maglev Transportation Technology,Qingdao City,China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101804452","display_name":"Xindong Wang","orcid":"https://orcid.org/0000-0002-7805-5779"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xindong Wang","raw_affiliation_strings":["CRRC Qingdao Sifang Co., Ltd.,State Key Laboratory of High-speed Maglev Transportation Technology,Qingdao City,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"CRRC Qingdao Sifang Co., Ltd.,State Key Laboratory of High-speed Maglev Transportation Technology,Qingdao City,China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"391","last_page":"395"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T14225","display_name":"Advanced Sensor and Control Systems","score":0.9150999784469604,"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/T14225","display_name":"Advanced Sensor and Control Systems","score":0.9150999784469604,"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/fault","display_name":"Fault (geology)","score":0.5228897929191589},{"id":"https://openalex.org/keywords/residual-neural-network","display_name":"Residual neural network","score":0.4801901578903198},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4744913578033447},{"id":"https://openalex.org/keywords/fault-detection-and-isolation","display_name":"Fault detection and isolation","score":0.45083391666412354},{"id":"https://openalex.org/keywords/suspension","display_name":"Suspension (topology)","score":0.4377220869064331},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.15553948283195496},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.09453099966049194},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.08727234601974487},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.08458316326141357},{"id":"https://openalex.org/keywords/actuator","display_name":"Actuator","score":0.05491665005683899}],"concepts":[{"id":"https://openalex.org/C175551986","wikidata":"https://www.wikidata.org/wiki/Q47089","display_name":"Fault (geology)","level":2,"score":0.5228897929191589},{"id":"https://openalex.org/C2944601119","wikidata":"https://www.wikidata.org/wiki/Q43744058","display_name":"Residual neural network","level":3,"score":0.4801901578903198},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4744913578033447},{"id":"https://openalex.org/C152745839","wikidata":"https://www.wikidata.org/wiki/Q5438153","display_name":"Fault detection and isolation","level":3,"score":0.45083391666412354},{"id":"https://openalex.org/C105341887","wikidata":"https://www.wikidata.org/wiki/Q1307987","display_name":"Suspension (topology)","level":3,"score":0.4377220869064331},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.15553948283195496},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.09453099966049194},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.08727234601974487},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.08458316326141357},{"id":"https://openalex.org/C172707124","wikidata":"https://www.wikidata.org/wiki/Q423488","display_name":"Actuator","level":2,"score":0.05491665005683899},{"id":"https://openalex.org/C5961521","wikidata":"https://www.wikidata.org/wiki/Q746083","display_name":"Homotopy","level":2,"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/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icmlca63499.2024.10753882","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmlca63499.2024.10753882","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 5th International Conference on Machine Learning and Computer Application (ICMLCA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2599472179","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890"],"abstract_inverted_index":{"The":[0,88,104,129],"suspension":[1,62,66,100,121],"controller":[2,101],"is":[3,124,135],"a":[4,27,37,97,110],"critical":[5],"component":[6],"of":[7,49,64,113,120],"high-speed":[8,98],"maglev":[9,99],"trains,":[10],"and":[11,16,31,61,74,77,118],"its":[12],"real-time":[13],"fault":[14,42,86,116],"diagnosis":[15,43,117],"reliability":[17],"are":[18],"crucial.":[19],"This":[20],"method":[21,90],"utilizes":[22],"ResNet":[23,38,68],"modules":[24],"to":[25,40,71,81],"build":[26],"deep":[28],"learning":[29],"model":[30],"uses":[32],"Kolmogorov-Arnold":[33],"Networks":[34],"(KAN)":[35],"as":[36],"classifier":[39],"improve":[41],"accuracy.":[44],"A":[45],"training":[46],"sample":[47],"set":[48],"multi-parameter":[50],"data":[51,95],"was":[52,69,79,91],"established,":[53],"including":[54],"current,":[55],"acceleration,":[56],"voltage,":[57],"resistance,":[58],"running":[59],"speed,":[60],"gap":[63],"the":[65],"controller.":[67],"used":[70,80],"extract":[72],"temporal":[73],"multi-dimensional":[75],"features,":[76],"KAN":[78],"fuse":[82],"these":[83],"features":[84],"for":[85],"characteristics.":[87],"proposed":[89],"validated":[92],"using":[93],"experimental":[94],"from":[96],"test":[102],"platform.":[103],"results":[105],"show":[106],"that":[107],"KAN-Resnet":[108],"has":[109],"high":[111],"accuracy":[112],"98.38%":[114],"in":[115],"inference":[119,130],"controllers,":[122],"which":[123,134],"5.54%":[125],"higher":[126,137],"than":[127,138],"7-layer-CNN.":[128],"speed":[131],"reaches":[132],"804n/s,":[133],"42.80%":[136],"ResNet-50.":[139]},"counts_by_year":[{"year":2025,"cited_by_count":5}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}