{"id":"https://openalex.org/W7154160643","doi":"https://doi.org/10.46281/bjmsr.v11i2.2851","title":"EARLY FAULT SEVERITY PREDICTION IN DIGITAL-TWIN\u2013ENABLED INDUSTRIAL SYSTEMS USING IMBALANCE-AWARE MACHINE LEARNING AND TEMPORAL FEATURE ENGINEERING","display_name":"EARLY FAULT SEVERITY PREDICTION IN DIGITAL-TWIN\u2013ENABLED INDUSTRIAL SYSTEMS USING IMBALANCE-AWARE MACHINE LEARNING AND TEMPORAL FEATURE ENGINEERING","publication_year":2026,"publication_date":"2026-04-13","ids":{"openalex":"https://openalex.org/W7154160643","doi":"https://doi.org/10.46281/bjmsr.v11i2.2851"},"language":"en","primary_location":{"id":"doi:10.46281/bjmsr.v11i2.2851","doi":"https://doi.org/10.46281/bjmsr.v11i2.2851","is_oa":true,"landing_page_url":"https://doi.org/10.46281/bjmsr.v11i2.2851","pdf_url":null,"source":{"id":"https://openalex.org/S4210198050","display_name":"Bangladesh Journal of Multidisciplinary Scientific Research","issn_l":"2687-850X","issn":["2687-850X","2687-8518"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Bangladesh Journal of Multidisciplinary Scientific Research","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://doi.org/10.46281/bjmsr.v11i2.2851","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5118453279","display_name":"Oualid Ali","orcid":"https://orcid.org/0009-0001-1433-017X","observed_orcids":["https://orcid.org/0009-0001-1433-017X"]},"institutions":[{"id":"https://openalex.org/I4210163116","display_name":"Applied Science University","ror":"https://ror.org/059zrbe49","country_code":"BH","type":"education","lineage":["https://openalex.org/I4210163116"]}],"countries":["BH"],"is_corresponding":true,"raw_author_name":"Oualid  Ali","raw_affiliation_strings":["Head of Computer Sciences Department, College of Arts & Science, Applied Science University, Manama, Bahrain"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Head of Computer Sciences Department, College of Arts & Science, Applied Science University, Manama, Bahrain","institution_ids":["https://openalex.org/I4210163116"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5118453279"],"corresponding_institution_ids":["https://openalex.org/I4210163116"],"apc_list":null,"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":"11","issue":"2","first_page":"28","last_page":"36"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10876","display_name":"Fault Detection and Control Systems","score":0.5907999873161316,"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/T10876","display_name":"Fault Detection and Control Systems","score":0.5907999873161316,"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/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.3589000105857849,"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/T10763","display_name":"Digital Transformation in Industry","score":0.017100000753998756,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing 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/digital-twin","display_name":"digital twin","score":0.7630000114440918},{"id":"https://openalex.org/keywords/predictive-maintenance","display_name":"predictive maintenance","score":0.9800000190734863},{"id":"https://openalex.org/keywords/class-imbalance","display_name":"class imbalance","score":0.8849999904632568},{"id":"https://openalex.org/keywords/temporal-feature-engineering","display_name":"temporal feature engineering","score":0.9089999794960022},{"id":"https://openalex.org/keywords/probability-calibration","display_name":"probability calibration","score":0.9869999885559082},{"id":"https://openalex.org/keywords/minority-class-detection","display_name":"minority class detection","score":0.7179999947547913},{"id":"https://openalex.org/keywords/industry-4-0","display_name":"Industry 4.0","score":0.8799999952316284}],"concepts":[{"id":"https://openalex.org/C175551986","wikidata":"https://www.wikidata.org/wiki/Q47089","display_name":"Fault (geology)","level":2,"score":0.6155999898910522},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.578499972820282},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.5105000138282776},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4902999997138977},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.43950000405311584},{"id":"https://openalex.org/C70452415","wikidata":"https://www.wikidata.org/wiki/Q3182448","display_name":"Predictive maintenance","level":2,"score":0.42100000381469727},{"id":"https://openalex.org/C200601418","wikidata":"https://www.wikidata.org/wiki/Q2193887","display_name":"Reliability engineering","level":1,"score":0.41999998688697815},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.41449999809265137},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.413100004196167},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4065999984741211},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.3817000091075897},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.35670000314712524},{"id":"https://openalex.org/C152745839","wikidata":"https://www.wikidata.org/wiki/Q5438153","display_name":"Fault detection and isolation","level":3,"score":0.35420000553131104},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.3479999899864197},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.3416000008583069},{"id":"https://openalex.org/C100660578","wikidata":"https://www.wikidata.org/wiki/Q18733","display_name":"Recall","level":2,"score":0.3352999985218048},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.3312999904155731},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.31310001015663147},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.3109999895095825},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.29420000314712524},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.29120001196861267},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.271699994802475},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.26910001039505005},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.26660001277923584},{"id":"https://openalex.org/C59594135","wikidata":"https://www.wikidata.org/wiki/Q5249242","display_name":"Decision model","level":2,"score":0.2653000056743622},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.26460000872612},{"id":"https://openalex.org/C126953365","wikidata":"https://www.wikidata.org/wiki/Q5438152","display_name":"Fault coverage","level":3,"score":0.2565000057220459},{"id":"https://openalex.org/C107327155","wikidata":"https://www.wikidata.org/wiki/Q330268","display_name":"Decision support system","level":2,"score":0.25049999356269836}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.46281/bjmsr.v11i2.2851","doi":"https://doi.org/10.46281/bjmsr.v11i2.2851","is_oa":true,"landing_page_url":"https://doi.org/10.46281/bjmsr.v11i2.2851","pdf_url":null,"source":{"id":"https://openalex.org/S4210198050","display_name":"Bangladesh Journal of Multidisciplinary Scientific Research","issn_l":"2687-850X","issn":["2687-850X","2687-8518"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Bangladesh Journal of Multidisciplinary Scientific Research","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.46281/bjmsr.v11i2.2851","doi":"https://doi.org/10.46281/bjmsr.v11i2.2851","is_oa":true,"landing_page_url":"https://doi.org/10.46281/bjmsr.v11i2.2851","pdf_url":null,"source":{"id":"https://openalex.org/S4210198050","display_name":"Bangladesh Journal of Multidisciplinary Scientific Research","issn_l":"2687-850X","issn":["2687-850X","2687-8518"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Bangladesh Journal of Multidisciplinary Scientific Research","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","score":0.7207000255584717,"id":"https://metadata.un.org/sdg/9"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W7123351507","https://openalex.org/W4319300573","https://openalex.org/W3111668058","https://openalex.org/W4367843921","https://openalex.org/W2992124668","https://openalex.org/W7006394669","https://openalex.org/W4406549665","https://openalex.org/W4404775525","https://openalex.org/W4405088235","https://openalex.org/W7213862922"],"abstract_inverted_index":{"Continuous":[0],"industrial":[1],"telemetry":[2],"in":[3,96,137],"high":[4],"dimensions":[5],"generated":[6],"by":[7],"digital":[8],"twin":[9],"(DT)":[10],"platforms":[11],"offers":[12],"opportunities":[13],"for":[14,225],"large-scale":[15],"fault":[16,39,78,84,158,171,177],"and":[17,73,81,118,144,162,197,220],"predictive":[18],"maintenance":[19,72,223],"(PdM).":[20],"However,":[21],"the":[22,35,126,134,142,189],"real-life":[23],"applications":[24],"of":[25,37,47,87,107,129,133,179,184,192],"PdM":[26],"remain":[27],"very":[28],"challenging.":[29],"In":[30],"this":[31,97,138,185],"paper,":[32],"we":[33],"investigate":[34],"potential":[36],"early":[38,202],"severity":[40,203],"prediction":[41,204],"using":[42,104,113],"a":[43,82,105,119,175,212],"five-year,":[44],"hourly":[45],"dataset":[46],"DT-derived":[48],"data":[49,57],"from":[50],"January":[51,54],"2019":[52],"to":[53,160,164,210],"2024.":[55],"The":[56,93,131,181],"includes":[58],"38":[59],"variables,":[60],"including":[61],"measurements":[62],"such":[63],"as":[64],"vibration,":[65],"temperature,":[66],"pressure,":[67],"acoustic":[68],"signals,":[69],"operational":[70],"load,":[71],"repair":[74],"history,":[75],"anomaly":[76],"scores,":[77],"probability":[79,116],"estimates,":[80],"multiclass":[83],"diagnosis":[85],"label":[86],"No":[88],"Fault":[89],"or":[90],"Critical":[91],"Fault.":[92],"research":[94,139,207],"presented":[95],"paper":[98],"follows":[99],"an":[100],"end-to-end":[101],"analytical":[102],"approach,":[103],"set":[106],"machine-learning":[108],"classifiers,":[109,115],"then":[110],"optimizing":[111],"them":[112],"class-weighted":[114,152],"calibration,":[117],"cost-sensitive":[120],"decision":[121,199],"policy,":[122],"all":[123],"aligned":[124],"with":[125,174],"strategic":[127],"goals":[128],"PdM.":[130],"results":[132],"experiments":[135],"conducted":[136],"show":[140,187],"that":[141,188,216],"baseline":[143],"temporally":[145],"enhanced":[146],"models":[147],"achieve":[148],"\u22480.69":[149],"accuracy.":[150],"When":[151],"learning":[153],"is":[154,208],"introduced,":[155],"it":[156],"increases":[157],"recall":[159,172],"0.059":[161],"macro-F1":[163],"0.451,":[165],"while":[166],"threshold":[167],"optimization":[168],"achieves":[169],"perfect":[170],"(1.00)":[173],"corresponding":[176],"F1-score":[178],"0.468.":[180],"main":[182],"findings":[183],"work":[186],"joint":[190],"effects":[191],"temporal":[193],"modelling,":[194],"imbalance-aware":[195],"learning,":[196],"calibrated":[198],"thresholds":[200],"govern":[201],"performance.":[205],"Our":[206],"expected":[209],"yield":[211],"reproducible,":[213],"deployment-oriented":[214],"framework":[215],"improves":[217],"minority-class":[218],"detection":[219],"enables":[221],"effective":[222],"decisions":[224],"DT-enabling":[226],"Industry":[227],"4.0/5.0":[228],"applications.":[229]},"counts_by_year":[],"updated_date":"2026-10-28T10:33:24.094632","created_date":"2026-04-14T00:00:00"}