{"id":"https://openalex.org/W7207842335","doi":"https://doi.org/10.1177/09544062261481213","title":"Cross-load robustness evaluation of deep and handcrafted learning frameworks for bearing fault diagnosis","display_name":"Cross-load robustness evaluation of deep and handcrafted learning frameworks for bearing fault diagnosis","publication_year":2026,"publication_date":"2026-09-02","ids":{"openalex":"https://openalex.org/W7207842335","doi":"https://doi.org/10.1177/09544062261481213"},"language":"en","primary_location":{"id":"doi:10.1177/09544062261481213","doi":"https://doi.org/10.1177/09544062261481213","is_oa":false,"landing_page_url":"https://doi.org/10.1177/09544062261481213","pdf_url":null,"source":{"id":"https://openalex.org/S164426212","display_name":"Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science","issn_l":"0263-7154","issn":["0263-7154","0954-4062","2041-2983","2058-3397"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","dongbi-d","jufo-1","norway-1","poland-70","russia-white-list-2"],"host_organization":"https://openalex.org/P4310320017","host_organization_name":"SAGE Publishing","host_organization_lineage":["https://openalex.org/P4310320017"],"host_organization_lineage_names":["SAGE Publishing"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"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/A5088243862","display_name":"Rohit Mishra","orcid":"https://orcid.org/0000-0002-0067-1205","observed_orcids":["https://orcid.org/0000-0002-0067-1205"]},"institutions":[{"id":"https://openalex.org/I25205351","display_name":"Jaypee University of Engineering and Technology, Guna","ror":"https://ror.org/040jmyh64","country_code":"IN","type":"education","lineage":["https://openalex.org/I25205351"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Rohit Mishra","raw_affiliation_strings":["Department of Mechanical Engineering, Jaypee University of Engineering and Technology, Guna, Madhya Pradesh, India"],"raw_orcid":"https://orcid.org/0000-0002-0067-1205","affiliations":[{"raw_affiliation_string":"Department of Mechanical Engineering, Jaypee University of Engineering and Technology, Guna, Madhya Pradesh, India","institution_ids":["https://openalex.org/I25205351"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025160354","display_name":"Bhagat Singh","orcid":"https://orcid.org/0000-0002-2012-1462","observed_orcids":["https://orcid.org/0000-0002-2012-1462"]},"institutions":[{"id":"https://openalex.org/I25205351","display_name":"Jaypee University of Engineering and Technology, Guna","ror":"https://ror.org/040jmyh64","country_code":"IN","type":"education","lineage":["https://openalex.org/I25205351"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Bhagat Singh","raw_affiliation_strings":["Department of Mechanical Engineering, Jaypee University of Engineering and Technology, Guna, Madhya Pradesh, India"],"raw_orcid":"https://orcid.org/0000-0002-2012-1462","affiliations":[{"raw_affiliation_string":"Department of Mechanical Engineering, Jaypee University of Engineering and Technology, Guna, Madhya Pradesh, India","institution_ids":["https://openalex.org/I25205351"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5044196068","display_name":"Yogesh Shrivastava","orcid":"https://orcid.org/0000-0002-4037-2680","observed_orcids":["https://orcid.org/0000-0002-4037-2680"]},"institutions":[{"id":"https://openalex.org/I163072128","display_name":"Galgotias College of Engineering & Technology","ror":"https://ror.org/04a85ht85","country_code":"IN","type":"education","lineage":["https://openalex.org/I163072128"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Yogesh Shrivastava","raw_affiliation_strings":["Department of Mechanical Engineering, Galgotias College of Engineering and Technology, Greater Noida, Uttar Pradesh, India"],"raw_orcid":"https://orcid.org/0000-0002-4037-2680","affiliations":[{"raw_affiliation_string":"Department of Mechanical Engineering, Galgotias College of Engineering and Technology, Greater Noida, Uttar Pradesh, India","institution_ids":["https://openalex.org/I163072128"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5044196068"],"corresponding_institution_ids":["https://openalex.org/I163072128"],"apc_list":{"value":4150,"currency":"USD","value_usd":4150},"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"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":1.0,"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":1.0,"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":0.0,"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/T11062","display_name":"Gear and Bearing Dynamics Analysis","score":0.0,"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/bearing-fault-diagnosis","display_name":"bearing fault diagnosis","score":1.0},{"id":"https://openalex.org/keywords/information-leakage","display_name":"information leakage","score":0.7789999842643738},{"id":"https://openalex.org/keywords/vibration-signal-analysis","display_name":"vibration signal analysis","score":0.5289999842643738},{"id":"https://openalex.org/keywords/multilayer-perceptron","display_name":"multilayer perceptron","score":0.9959999918937683},{"id":"https://openalex.org/keywords/cwru-bearing-dataset","display_name":"CWRU bearing dataset","score":0.8159999847412109},{"id":"https://openalex.org/keywords/paderborn-bearing-dataset","display_name":"Paderborn bearing dataset","score":0.8769999742507935},{"id":"https://openalex.org/keywords/external-validation","display_name":"external validation","score":0.656000018119812},{"id":"https://openalex.org/keywords/shap","display_name":"SHAP","score":0.5249999761581421},{"id":"https://openalex.org/keywords/permutation-feature-importance","display_name":"permutation importance","score":0.9850000143051147},{"id":"https://openalex.org/keywords/interpretability","display_name":"interpretability","score":0.5950000286102295},{"id":"https://openalex.org/keywords/computational-complexity","display_name":"computational complexity","score":0.718999981880188}],"concepts":[{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.7476999759674072},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6628000140190125},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6359000205993652},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.623199999332428},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5424000024795532},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.5084999799728394},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4722999930381775},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.46560001373291016},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4332999885082245},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.41839998960494995},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.4169999957084656},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.40220001339912415},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3774999976158142},{"id":"https://openalex.org/C199978012","wikidata":"https://www.wikidata.org/wiki/Q1273815","display_name":"Bearing (navigation)","level":2,"score":0.36809998750686646},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.3540000021457672},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.35179999470710754},{"id":"https://openalex.org/C179717631","wikidata":"https://www.wikidata.org/wiki/Q2991667","display_name":"Multilayer perceptron","level":3,"score":0.3499999940395355},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.3100000023841858},{"id":"https://openalex.org/C175551986","wikidata":"https://www.wikidata.org/wiki/Q47089","display_name":"Fault (geology)","level":2,"score":0.30799999833106995},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.2944999933242798},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.2935999929904938},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.27900001406669617},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.27730000019073486},{"id":"https://openalex.org/C2779915298","wikidata":"https://www.wikidata.org/wiki/Q7604400","display_name":"Statistical learning theory","level":3,"score":0.2700999975204468},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.26330000162124634},{"id":"https://openalex.org/C152745839","wikidata":"https://www.wikidata.org/wiki/Q5438153","display_name":"Fault detection and isolation","level":3,"score":0.26260000467300415}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1177/09544062261481213","doi":"https://doi.org/10.1177/09544062261481213","is_oa":false,"landing_page_url":"https://doi.org/10.1177/09544062261481213","pdf_url":null,"source":{"id":"https://openalex.org/S164426212","display_name":"Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science","issn_l":"0263-7154","issn":["0263-7154","0954-4062","2041-2983","2058-3397"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","dongbi-d","jufo-1","norway-1","poland-70","russia-white-list-2"],"host_organization":"https://openalex.org/P4310320017","host_organization_name":"SAGE Publishing","host_organization_lineage":["https://openalex.org/P4310320017"],"host_organization_lineage_names":["SAGE Publishing"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W243674440","https://openalex.org/W2040870580","https://openalex.org/W2736186771","https://openalex.org/W2912412749","https://openalex.org/W3024441147","https://openalex.org/W3191136867","https://openalex.org/W4205093204","https://openalex.org/W4206166171","https://openalex.org/W4213346827","https://openalex.org/W4221130231","https://openalex.org/W4225815954","https://openalex.org/W4289731814","https://openalex.org/W4295126433","https://openalex.org/W4297902741","https://openalex.org/W4322773144","https://openalex.org/W4327620151","https://openalex.org/W4366976518","https://openalex.org/W4378807694","https://openalex.org/W4380882485","https://openalex.org/W4391034982","https://openalex.org/W4391037910","https://openalex.org/W4396579823","https://openalex.org/W4400800375","https://openalex.org/W4402651662","https://openalex.org/W4405238643","https://openalex.org/W4405270134","https://openalex.org/W4405674384","https://openalex.org/W4411179406","https://openalex.org/W4411306530","https://openalex.org/W4411665802","https://openalex.org/W4414530328","https://openalex.org/W4417228809","https://openalex.org/W7083708274","https://openalex.org/W7117121194","https://openalex.org/W7133646573"],"related_works":["https://openalex.org/W2920611841","https://openalex.org/W2887767982","https://openalex.org/W4313281834","https://openalex.org/W7204818328","https://openalex.org/W7160932700","https://openalex.org/W4390920475","https://openalex.org/W4366976518","https://openalex.org/W7222577775","https://openalex.org/W3090682168","https://openalex.org/W7222925915"],"abstract_inverted_index":{"This":[0],"paper":[1],"introduces":[2],"a":[3,202],"comprehensive":[4],"leakage-free":[5],"methodology":[6],"for":[7,55,138,181,213],"bearing":[8,53,158,215],"fault":[9,216],"diagnosis":[10],"by":[11],"three":[12,142],"of":[13,99,108,133,140,188],"the":[14,47,76,83,97,100,114,130,141,148,155,185,189,196],"most":[15],"representative":[16],"feature":[17,126],"learning":[18,164],"approaches:":[19],"Raw":[20,104],"Signal":[21],"Convolutional":[22],"Neural":[23],"Network":[24],"(Raw":[25],"CNN),":[26],"Fast":[27],"Fourier":[28],"Transform":[29],"(FFT)":[30],"based":[31,38,127],"hybrid":[32],"MLP-CNN,":[33],"and":[34,62,80,88,111,122,136,167,210],"handcrafted":[35,125,197],"statistical":[36,198],"descriptor":[37],"Multilayer":[39],"Perceptron":[40],"(MLP).":[41],"The":[42,103,124,175,192],"vibration":[43],"data":[44],"collected":[45],"from":[46],"Case":[48],"Western":[49],"Reserve":[50],"University":[51,157],"(CWRU)":[52],"set":[54],"four":[56],"operating":[57],"conditions":[58,85],"(0,":[59],"1,":[60,86],"2,":[61,87],"3":[63,89],"HP)":[64],"was":[65],"used.":[66],"To":[67],"eliminate":[68],"information":[69],"leakage,":[70],"all":[71],"models":[72,101],"were":[73,92,173],"trained":[74],"under":[75,82],"base-line":[77],"condition":[78],"0":[79],"tested":[81],"unseen":[84],"HP,":[90],"which":[91],"cross":[93],"loads":[94],"to":[95,119,146],"assess":[96],"robustness":[98],"objectively.":[102],"CNN":[105],"achieved":[106],"accuracies":[107],"59.18%,":[109],"59.84%,":[110],"61.92%,":[112],"while":[113],"FFT-based":[115],"MLP-CNN":[116],"improved":[117],"performance":[118],"79.92%,":[120],"77.01%,":[121],"65.22%.":[123],"MLP":[128],"gave":[129],"best":[131],"accuracy":[132,180],"99.47%,":[134],"89.43%,":[135],"78.92%":[137],"each":[139],"datasets.":[143],"In":[144],"order":[145],"support":[147],"evaluation,":[149],"computational":[150],"complexity,":[151],"external":[152,176],"validation":[153,177],"with":[154,161,201],"Paderborn":[156],"dataset,":[159],"comparison":[160],"alternative":[162],"machine":[163],"classifiers,":[165],"SHAP,":[166],"Permutation":[168],"Feature":[169],"Importance":[170],"(PFI)":[171],"analyses":[172],"performed.":[174],"yielded":[178],"98.76%":[179],"classification,":[182],"further":[183],"confirming":[184],"generalization":[186],"capability":[187],"proposed":[190],"framework.":[191],"results":[193],"show":[194],"that":[195],"descriptors,":[199],"along":[200],"simple":[203],"MLP,":[204],"is":[205],"an":[206],"interpretable,":[207],"computationally":[208],"efficient,":[209],"robust":[211],"method":[212],"cross-load":[214],"diagnosis.":[217]},"counts_by_year":[],"updated_date":"2026-10-27T15:51:10.671819","created_date":"2026-09-04T00:00:00"}