{"id":"https://openalex.org/W4284889323","doi":"https://doi.org/10.1109/icphm53196.2022.9815765","title":"Deep Feature Learning Based Fault Detection with High-Frequency Signals","display_name":"Deep Feature Learning Based Fault Detection with High-Frequency Signals","publication_year":2022,"publication_date":"2022-06-06","ids":{"openalex":"https://openalex.org/W4284889323","doi":"https://doi.org/10.1109/icphm53196.2022.9815765"},"language":"en","primary_location":{"id":"doi:10.1109/icphm53196.2022.9815765","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icphm53196.2022.9815765","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Prognostics and Health Management (ICPHM)","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/A5114734261","display_name":"Zhengyi Jiang","orcid":"https://orcid.org/0000-0002-2060-1108"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhengyi Jiang","raw_affiliation_strings":["Tsinghua University,Department of Automation,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University,Department of Automation,Beijing,China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067661295","display_name":"Chongdang Liu","orcid":"https://orcid.org/0000-0001-5728-4890"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chongdang Liu","raw_affiliation_strings":["Tsinghua University,Department of Automation,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University,Department of Automation,Beijing,China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5010609544","display_name":"Linxuan Zhang","orcid":"https://orcid.org/0000-0001-5204-1347"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Linxuan Zhang","raw_affiliation_strings":["Tsinghua University,Department of Automation,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University,Department of Automation,Beijing,China","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":0.1687,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.38582829,"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":"101","last_page":"107"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9951000213623047,"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":0.9951000213623047,"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/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.9837999939918518,"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/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.970300018787384,"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/computer-science","display_name":"Computer science","score":0.7717339992523193},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.7025783061981201},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.69588303565979},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.6551041603088379},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6436659693717957},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6422613859176636},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.5702735781669617},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5566782355308533},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.5125055313110352},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.49537885189056396},{"id":"https://openalex.org/keywords/layer","display_name":"Layer (electronics)","score":0.490508496761322},{"id":"https://openalex.org/keywords/discrete-wavelet-transform","display_name":"Discrete wavelet transform","score":0.48811230063438416},{"id":"https://openalex.org/keywords/frequency-domain","display_name":"Frequency domain","score":0.4718708395957947},{"id":"https://openalex.org/keywords/fault","display_name":"Fault (geology)","score":0.43746697902679443},{"id":"https://openalex.org/keywords/wavelet","display_name":"Wavelet","score":0.40800216794013977},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.34710583090782166},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3362167775630951},{"id":"https://openalex.org/keywords/wavelet-transform","display_name":"Wavelet transform","score":0.2903805375099182},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.182582288980484}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7717339992523193},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.7025783061981201},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.69588303565979},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.6551041603088379},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6436659693717957},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6422613859176636},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.5702735781669617},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5566782355308533},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.5125055313110352},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.49537885189056396},{"id":"https://openalex.org/C2779227376","wikidata":"https://www.wikidata.org/wiki/Q6505497","display_name":"Layer (electronics)","level":2,"score":0.490508496761322},{"id":"https://openalex.org/C46286280","wikidata":"https://www.wikidata.org/wiki/Q2414958","display_name":"Discrete wavelet transform","level":4,"score":0.48811230063438416},{"id":"https://openalex.org/C19118579","wikidata":"https://www.wikidata.org/wiki/Q786423","display_name":"Frequency domain","level":2,"score":0.4718708395957947},{"id":"https://openalex.org/C175551986","wikidata":"https://www.wikidata.org/wiki/Q47089","display_name":"Fault (geology)","level":2,"score":0.43746697902679443},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.40800216794013977},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34710583090782166},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3362167775630951},{"id":"https://openalex.org/C196216189","wikidata":"https://www.wikidata.org/wiki/Q2867","display_name":"Wavelet transform","level":3,"score":0.2903805375099182},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.182582288980484},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","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},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","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/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icphm53196.2022.9815765","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icphm53196.2022.9815765","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Prognostics and Health Management (ICPHM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.41999998688697815,"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W2024240534","https://openalex.org/W2132984323","https://openalex.org/W2146842127","https://openalex.org/W2201092681","https://openalex.org/W2314054550","https://openalex.org/W2375629131","https://openalex.org/W2608144642","https://openalex.org/W2735879256","https://openalex.org/W2889054295","https://openalex.org/W2889448589","https://openalex.org/W2905949437","https://openalex.org/W2962933664","https://openalex.org/W2966556099","https://openalex.org/W3006867203","https://openalex.org/W3042309388","https://openalex.org/W3093522608","https://openalex.org/W3098434383","https://openalex.org/W4214806317","https://openalex.org/W4297779254","https://openalex.org/W4385245566","https://openalex.org/W6687630728","https://openalex.org/W6733557825","https://openalex.org/W6739901393"],"related_works":["https://openalex.org/W2081900870","https://openalex.org/W2382174632","https://openalex.org/W2129959498","https://openalex.org/W183670115","https://openalex.org/W1501179639","https://openalex.org/W3199035354","https://openalex.org/W1807354010","https://openalex.org/W3143644526","https://openalex.org/W598225674","https://openalex.org/W2734230146"],"abstract_inverted_index":{"High-frequency":[0],"signals,":[1],"which":[2,55,99,127],"play":[3],"an":[4,62],"important":[5],"role":[6],"in":[7],"detecting":[8],"the":[9,40,91,107,121],"occurrence":[10],"of":[11,16,32,42,82,110],"early":[12],"faults,":[13],"are":[14],"characteristic":[15],"massive":[17],"sparse":[18],"features.":[19,38,104],"Existing":[20],"feature":[21,52],"extraction":[22],"methods":[23,98],"mainly":[24],"focus":[25],"on":[26,77],"selecting":[27],"from":[28],"a":[29,50,66,78,134],"large":[30],"quantity":[31],"statistics":[33,101],"or":[34],"designing":[35],"elaborate":[36],"hand-crafted":[37,93],"With":[39],"help":[41],"little":[43],"prior":[44],"domain":[45],"knowledge,":[46],"this":[47],"paper":[48],"proposes":[49],"deep":[51,130],"learning":[53],"approach":[54,89,117],"employs":[56],"discrete":[57],"wavelet":[58],"transform":[59],"(DWT)":[60],"as":[61,102],"embedding":[63],"layer":[64,112],"for":[65],"bidirectional":[67],"gated":[68],"recurrent":[69],"unit":[70],"network":[71],"with":[72],"attention":[73,111],"mechanism":[74],"(AM-BiGRU).":[75],"Experiments":[76],"real-world":[79],"case":[80],"study":[81],"partial":[83],"discharge":[84],"(PD)":[85],"verify":[86],"that":[87,115],"our":[88,116,129],"outperforms":[90],"state-of-the-art":[92],"features":[94],"and":[95],"other":[96],"data-driven":[97],"select":[100],"shallow":[103],"In":[105],"addition,":[106],"visualization":[108],"result":[109],"weightings":[113],"confirms":[114],"could":[118],"automatically":[119],"locate":[120],"area":[122],"where":[123],"faults":[124],"may":[125],"occur,":[126],"makes":[128],"model":[131],"explicable":[132],"to":[133],"certain":[135],"extent.":[136]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
