{"id":"https://openalex.org/W4391496286","doi":"https://doi.org/10.1109/m2vip58386.2023.10413425","title":"A Deep Learning Based Fault Diagnosis Method Combining Domain Knowledge and Transfer Learning","display_name":"A Deep Learning Based Fault Diagnosis Method Combining Domain Knowledge and Transfer Learning","publication_year":2023,"publication_date":"2023-11-21","ids":{"openalex":"https://openalex.org/W4391496286","doi":"https://doi.org/10.1109/m2vip58386.2023.10413425"},"language":"en","primary_location":{"id":"doi:10.1109/m2vip58386.2023.10413425","is_oa":false,"landing_page_url":"https://doi.org/10.1109/m2vip58386.2023.10413425","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 29th International Conference on Mechatronics and Machine Vision in Practice (M2VIP)","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/A5068507022","display_name":"Madhurjya Dev Choudhury","orcid":"https://orcid.org/0000-0003-1166-0963"},"institutions":[{"id":"https://openalex.org/I41156924","display_name":"Victoria University of Wellington","ror":"https://ror.org/0040r6f76","country_code":"NZ","type":"education","lineage":["https://openalex.org/I41156924"]}],"countries":["NZ"],"is_corresponding":true,"raw_author_name":"Madhurjya Dev Choudhury","raw_affiliation_strings":["School of Engineering and Computer Science, Victoria University of Wellington,New Zealand","School of Engineering and Computer Science, Victoria University of Wellington, New Zealand"],"affiliations":[{"raw_affiliation_string":"School of Engineering and Computer Science, Victoria University of Wellington,New Zealand","institution_ids":["https://openalex.org/I41156924"]},{"raw_affiliation_string":"School of Engineering and Computer Science, Victoria University of Wellington, New Zealand","institution_ids":["https://openalex.org/I41156924"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087492771","display_name":"W. Bastiaan Kleijn","orcid":"https://orcid.org/0000-0002-1973-3920"},"institutions":[{"id":"https://openalex.org/I41156924","display_name":"Victoria University of Wellington","ror":"https://ror.org/0040r6f76","country_code":"NZ","type":"education","lineage":["https://openalex.org/I41156924"]}],"countries":["NZ"],"is_corresponding":false,"raw_author_name":"W. Bastiaan Kleijn","raw_affiliation_strings":["School of Engineering and Computer Science, Victoria University of Wellington,New Zealand","School of Engineering and Computer Science, Victoria University of Wellington, New Zealand"],"affiliations":[{"raw_affiliation_string":"School of Engineering and Computer Science, Victoria University of Wellington,New Zealand","institution_ids":["https://openalex.org/I41156924"]},{"raw_affiliation_string":"School of Engineering and Computer Science, Victoria University of Wellington, New Zealand","institution_ids":["https://openalex.org/I41156924"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039326697","display_name":"Kelly Blincoe","orcid":"https://orcid.org/0000-0003-4092-9706"},"institutions":[{"id":"https://openalex.org/I154130895","display_name":"University of Auckland","ror":"https://ror.org/03b94tp07","country_code":"NZ","type":"education","lineage":["https://openalex.org/I154130895"]}],"countries":["NZ"],"is_corresponding":false,"raw_author_name":"Kelly Blincoe","raw_affiliation_strings":["University of Auckland,Department of Electrical, Computer, and Software Engineering,New Zealand","Department of Electrical, Computer, and Software Engineering, University of Auckland, New Zealand"],"affiliations":[{"raw_affiliation_string":"University of Auckland,Department of Electrical, Computer, and Software Engineering,New Zealand","institution_ids":["https://openalex.org/I154130895"]},{"raw_affiliation_string":"Department of Electrical, Computer, and Software Engineering, University of Auckland, New Zealand","institution_ids":["https://openalex.org/I154130895"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5010342778","display_name":"Jaspreet Singh Dhupia","orcid":"https://orcid.org/0000-0001-7181-1917"},"institutions":[{"id":"https://openalex.org/I154130895","display_name":"University of Auckland","ror":"https://ror.org/03b94tp07","country_code":"NZ","type":"education","lineage":["https://openalex.org/I154130895"]}],"countries":["NZ"],"is_corresponding":false,"raw_author_name":"Jaspreet Singh Dhupia","raw_affiliation_strings":["University of Auckland,Department of Mechanical and Mechatronics Engineering,New Zealand","Department of Mechanical and Mechatronics Engineering, University of Auckland, New Zealand"],"affiliations":[{"raw_affiliation_string":"University of Auckland,Department of Mechanical and Mechatronics Engineering,New Zealand","institution_ids":["https://openalex.org/I154130895"]},{"raw_affiliation_string":"Department of Mechanical and Mechatronics Engineering, University of Auckland, New Zealand","institution_ids":["https://openalex.org/I154130895"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5068507022"],"corresponding_institution_ids":["https://openalex.org/I41156924"],"apc_list":null,"apc_paid":null,"fwci":0.1876,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.52994206,"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":0.9980000257492065,"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":0.9980000257492065,"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/T13690","display_name":"Quality and Safety in Healthcare","score":0.9314000010490417,"subfield":{"id":"https://openalex.org/subfields/3607","display_name":"Medical Laboratory Technology"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10809","display_name":"Occupational Health and Safety Research","score":0.926800012588501,"subfield":{"id":"https://openalex.org/subfields/3614","display_name":"Radiological and Ultrasound Technology"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7104325294494629},{"id":"https://openalex.org/keywords/fault","display_name":"Fault (geology)","score":0.6937463283538818},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6782901287078857},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6742863655090332},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.6148462295532227},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5647008419036865},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5606938600540161},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.5288704037666321},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.489156037569046},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.48480668663978577},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.475980669260025},{"id":"https://openalex.org/keywords/cyclostationary-process","display_name":"Cyclostationary process","score":0.46190911531448364},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.46063244342803955},{"id":"https://openalex.org/keywords/domain-knowledge","display_name":"Domain knowledge","score":0.4516744017601013},{"id":"https://openalex.org/keywords/bearing","display_name":"Bearing (navigation)","score":0.4294793903827667},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.4129208028316498},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.2707708775997162},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.12136098742485046}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7104325294494629},{"id":"https://openalex.org/C175551986","wikidata":"https://www.wikidata.org/wiki/Q47089","display_name":"Fault (geology)","level":2,"score":0.6937463283538818},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6782901287078857},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6742863655090332},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.6148462295532227},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5647008419036865},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5606938600540161},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.5288704037666321},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.489156037569046},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.48480668663978577},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.475980669260025},{"id":"https://openalex.org/C178351263","wikidata":"https://www.wikidata.org/wiki/Q3922399","display_name":"Cyclostationary process","level":3,"score":0.46190911531448364},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.46063244342803955},{"id":"https://openalex.org/C207685749","wikidata":"https://www.wikidata.org/wiki/Q2088941","display_name":"Domain knowledge","level":2,"score":0.4516744017601013},{"id":"https://openalex.org/C199978012","wikidata":"https://www.wikidata.org/wiki/Q1273815","display_name":"Bearing (navigation)","level":2,"score":0.4294793903827667},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.4129208028316498},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.2707708775997162},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.12136098742485046},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","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/C165205528","wikidata":"https://www.wikidata.org/wiki/Q83371","display_name":"Seismology","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","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/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"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/m2vip58386.2023.10413425","is_oa":false,"landing_page_url":"https://doi.org/10.1109/m2vip58386.2023.10413425","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 29th International Conference on Mechatronics and Machine Vision in Practice (M2VIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320310281","display_name":"Case Western Reserve University","ror":"https://ror.org/051fd9666"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W243674440","https://openalex.org/W1964511482","https://openalex.org/W1986260315","https://openalex.org/W2112601224","https://openalex.org/W2485614840","https://openalex.org/W2798981066","https://openalex.org/W2804879845","https://openalex.org/W2887782657","https://openalex.org/W2967625104","https://openalex.org/W3126383074","https://openalex.org/W3128292762","https://openalex.org/W3161817677","https://openalex.org/W3200353131","https://openalex.org/W4200193210","https://openalex.org/W4210289118","https://openalex.org/W4212861124","https://openalex.org/W4223512636","https://openalex.org/W4385488423"],"related_works":["https://openalex.org/W943561131","https://openalex.org/W1490820195","https://openalex.org/W2360821999","https://openalex.org/W2364216765","https://openalex.org/W2533983735","https://openalex.org/W2002970502","https://openalex.org/W2363371188","https://openalex.org/W2977618689","https://openalex.org/W2440023763","https://openalex.org/W2962474440"],"abstract_inverted_index":{"Deep":[0],"learning":[1,23],"(DL)":[2],"based":[3],"fault":[4,51,62,131,153],"diagnosis":[5,28,63,132,166],"methods":[6],"have":[7],"gained":[8],"considerable":[9],"attention":[10],"in":[11,115,124,133,175],"the":[12,31,42,97,102,159],"field":[13],"of":[14,46,70,75,78,96,104,109],"machine":[15,47,82],"health":[16,44],"monitoring":[17],"due":[18],"to":[19,34],"their":[20],"powerful":[21],"feature":[22],"capabilities.":[24],"However,":[25],"embedding":[26],"domain":[27],"knowledge":[29,123],"into":[30],"DL":[32,127],"framework":[33,128],"obtain":[35],"enhanced":[36],"features":[37],"having":[38],"better":[39,171],"correlation":[40],"with":[41],"exact":[43],"conditions":[45],"elements":[48],"for":[49,101,129],"improved":[50],"predictions":[52],"is":[53,91,94,138],"still":[54],"an":[55,143],"open":[56],"challenge.":[57],"In":[58,117],"this":[59,118,122],"paper,":[60,119],"a":[61,80,85,126,164,170],"method":[64,137,161],"combining":[65],"two-dimensional":[66],"(2D)":[67],"image":[68],"representations":[69],"squared":[71],"envelop":[72],"spectrum":[73],"(SES)":[74],"vibration":[76,146],"signals":[77],"bearings,":[79],"critical":[81],"element,":[83],"and":[84,140,152,168,177],"pretrained":[86],"convolutional":[87],"neural":[88],"network":[89],"(CNN)":[90],"proposed.":[92],"SES":[93],"one":[95],"most":[98],"efficient":[99,130],"indicator":[100],"assessment":[103],"second":[105],"order":[106],"cyclostationary":[107],"symptoms":[108],"damages,":[110],"which":[111],"are":[112],"typically":[113],"observed":[114],"bearings.":[116,134],"we":[120],"integrate":[121],"designing":[125],"The":[135],"proposed":[136,160],"tested":[139],"evaluated":[141],"on":[142],"experimental":[144],"bearing":[145],"dataset":[147],"collected":[148],"under":[149],"different":[150],"operating":[151],"conditions.":[154],"Experimental":[155],"results":[156],"demonstrate":[157],"that":[158],"can":[162],"achieve":[163],"high":[165],"accuracy":[167],"present":[169],"generalization":[172],"ability":[173],"both":[174],"balanced":[176],"imbalanced":[178],"data":[179],"scenarios.":[180]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
