{"id":"https://openalex.org/W4395484808","doi":"https://doi.org/10.48550/arxiv.2404.15341","title":"Classifier-guided neural blind deconvolution: a physics-informed denoising module for bearing fault diagnosis under heavy noise","display_name":"Classifier-guided neural blind deconvolution: a physics-informed denoising module for bearing fault diagnosis under heavy noise","publication_year":2024,"publication_date":"2024-04-11","ids":{"openalex":"https://openalex.org/W4395484808","doi":"https://doi.org/10.48550/arxiv.2404.15341"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2404.15341","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2404.15341","pdf_url":"https://arxiv.org/pdf/2404.15341","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2404.15341","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5050878071","display_name":"Jing-Xiao Liao","orcid":"https://orcid.org/0000-0002-1880-2621"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Liao, Jing-Xiao","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030587116","display_name":"Chao He","orcid":"https://orcid.org/0000-0003-0008-6871"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"He, Chao","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037156903","display_name":"Jipu Li","orcid":"https://orcid.org/0000-0002-9113-1465"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Jipu","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111018262","display_name":"Jinwei Sun","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sun, Jinwei","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033281065","display_name":"Shiping Zhang","orcid":"https://orcid.org/0000-0001-9329-8894"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Shiping","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5080316854","display_name":"Xiaoge Zhang","orcid":"https://orcid.org/0000-0001-6831-3175"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Xiaoge","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5050878071"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":2,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.9940999746322632,"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":0.9940999746322632,"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/T10876","display_name":"Fault Detection and Control Systems","score":0.9929999709129333,"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/T10188","display_name":"Advanced machining processes and optimization","score":0.9262999892234802,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical 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/noise-reduction","display_name":"Noise reduction","score":0.616000771522522},{"id":"https://openalex.org/keywords/deconvolution","display_name":"Deconvolution","score":0.6134943962097168},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5934059023857117},{"id":"https://openalex.org/keywords/blind-deconvolution","display_name":"Blind deconvolution","score":0.5786751508712769},{"id":"https://openalex.org/keywords/bearing","display_name":"Bearing (navigation)","score":0.4861854910850525},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.44859635829925537},{"id":"https://openalex.org/keywords/fault","display_name":"Fault (geology)","score":0.4411020874977112},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.43090540170669556},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.43040943145751953},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.41175612807273865},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.2349793016910553},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.05803599953651428},{"id":"https://openalex.org/keywords/seismology","display_name":"Seismology","score":0.05311587452888489}],"concepts":[{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.616000771522522},{"id":"https://openalex.org/C174576160","wikidata":"https://www.wikidata.org/wiki/Q1183700","display_name":"Deconvolution","level":2,"score":0.6134943962097168},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5934059023857117},{"id":"https://openalex.org/C30044814","wikidata":"https://www.wikidata.org/wiki/Q11334452","display_name":"Blind deconvolution","level":3,"score":0.5786751508712769},{"id":"https://openalex.org/C199978012","wikidata":"https://www.wikidata.org/wiki/Q1273815","display_name":"Bearing (navigation)","level":2,"score":0.4861854910850525},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.44859635829925537},{"id":"https://openalex.org/C175551986","wikidata":"https://www.wikidata.org/wiki/Q47089","display_name":"Fault (geology)","level":2,"score":0.4411020874977112},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.43090540170669556},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.43040943145751953},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.41175612807273865},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2349793016910553},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.05803599953651428},{"id":"https://openalex.org/C165205528","wikidata":"https://www.wikidata.org/wiki/Q83371","display_name":"Seismology","level":1,"score":0.05311587452888489},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2404.15341","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2404.15341","pdf_url":"https://arxiv.org/pdf/2404.15341","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"doi:10.48550/arxiv.2404.15341","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2404.15341","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2404.15341","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2404.15341","pdf_url":"https://arxiv.org/pdf/2404.15341","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4395484808.pdf"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2034520358","https://openalex.org/W2028099218","https://openalex.org/W2059639161","https://openalex.org/W2586325539","https://openalex.org/W2058895457","https://openalex.org/W1538056422","https://openalex.org/W2029084792","https://openalex.org/W2023738877","https://openalex.org/W2147401053","https://openalex.org/W2589098082"],"abstract_inverted_index":{"Blind":[0],"deconvolution":[1],"(BD)":[2],"has":[3],"been":[4],"demonstrated":[5],"as":[6],"an":[7],"efficacious":[8],"approach":[9],"for":[10,54,91,132],"extracting":[11],"bearing":[12,221],"fault-specific":[13],"features":[14,195],"from":[15,226],"vibration":[16],"signals":[17],"under":[18,236],"strong":[19,200],"background":[20],"noise.":[21,201],"Despite":[22],"BD's":[23],"desirable":[24],"feature":[25,55,96],"in":[26,79],"adaptability":[27],"and":[28,61,98,108,127,169,179],"mathematical":[29],"interpretability,":[30],"a":[31,106,140,145,160,170],"significant":[32],"challenge":[33],"persists:":[34],"How":[35],"to":[36,116,143,149,173,190,193,220],"effectively":[37],"integrate":[38],"BD":[39,49,65,89,111,126,154,177,192,216],"with":[40,57,68],"fault-diagnosing":[41],"classifiers?":[42],"This":[43],"issue":[44],"arises":[45],"because":[46],"the":[47,73,122,128,151,176,184,203,210],"traditional":[48],"method":[50],"is":[51,66,209,217],"solely":[52],"designed":[53],"extraction":[56,97],"its":[58,213],"own":[59],"optimizer":[60],"objective":[62],"function.":[63],"When":[64],"combined":[67],"downstream":[69],"deep":[70,99,129,146,180],"learning":[71,75,93,130,147,152,181],"classifiers,":[72],"different":[74],"objectives":[76],"will":[77],"be":[78],"conflict.":[80],"To":[81,202],"address":[82],"this":[83,85,208],"problem,":[84],"paper":[86],"introduces":[87],"classifier-guided":[88],"(ClassBD)":[90],"joint":[92],"of":[94,125,134,153,165,205,212],"BD-based":[95],"learning-based":[100],"fault":[101,185,222],"classification.":[102],"Firstly,":[103],"we":[104,138,158],"present":[105],"time":[107],"frequency":[109],"neural":[110,114],"that":[112,196,215,230],"employs":[113],"networks":[115],"implement":[117],"conventional":[118],"BD,":[119],"thereby":[120],"facilitating":[121],"seamless":[123],"integration":[124],"classifier":[131,148],"co-optimization":[133],"model":[135],"parameters.":[136],"Subsequently,":[137],"develop":[139],"unified":[141],"framework":[142],"use":[144],"guide":[150],"filters.":[155],"In":[156],"addition,":[157],"devise":[159],"physics-informed":[161],"loss":[162,172],"function":[163],"composed":[164],"kurtosis,":[166],"$l_2/l_4$":[167],"norm,":[168],"cross-entropy":[171],"jointly":[174],"optimize":[175],"filters":[178],"classifier.":[182],"Consequently,":[183],"labels":[186],"provide":[187],"useful":[188],"information":[189],"direct":[191],"extract":[194],"distinguish":[197],"classes":[198],"amidst":[199],"best":[204],"our":[206],"knowledge,":[207],"first":[211],"kind":[214],"successfully":[218],"applied":[219],"diagnosis.":[223],"Experimental":[224],"results":[225],"three":[227],"datasets":[228],"demonstrate":[229],"ClassBD":[231],"outperforms":[232],"other":[233],"state-of-the-art":[234],"methods":[235],"noisy":[237],"conditions.":[238]},"counts_by_year":[{"year":2024,"cited_by_count":2}],"updated_date":"2026-03-11T14:59:36.786465","created_date":"2025-10-10T00:00:00"}
