{"id":"https://openalex.org/W7127985392","doi":"https://doi.org/10.1109/iccma67641.2025.11369643","title":"A Hybrid Weibull-Physics-Informed Neural Network Framework for Early Anomaly Detection in Lithium-Ion Batteries","display_name":"A Hybrid Weibull-Physics-Informed Neural Network Framework for Early Anomaly Detection in Lithium-Ion Batteries","publication_year":2025,"publication_date":"2025-11-24","ids":{"openalex":"https://openalex.org/W7127985392","doi":"https://doi.org/10.1109/iccma67641.2025.11369643"},"language":null,"primary_location":{"id":"doi:10.1109/iccma67641.2025.11369643","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccma67641.2025.11369643","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 13th International Conference on Control, Mechatronics and Automation (ICCMA)","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/A5124903125","display_name":"Haneen Altartouri","orcid":null},"institutions":[{"id":"https://openalex.org/I119744171","display_name":"Higher Colleges of Technology","ror":"https://ror.org/00qmy9z88","country_code":"AE","type":"education","lineage":["https://openalex.org/I119744171"]}],"countries":["AE"],"is_corresponding":false,"raw_author_name":"Haneen Altartouri","raw_affiliation_strings":["Fujairah University,College of Engineering and Technology,Fujairah,UAE"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fujairah University,College of Engineering and Technology,Fujairah,UAE","institution_ids":["https://openalex.org/I119744171"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043677461","display_name":"Sahar Qaadan","orcid":"https://orcid.org/0000-0002-1956-6376"},"institutions":[{"id":"https://openalex.org/I230091363","display_name":"German Jordanian University","ror":"https://ror.org/02jgpyd84","country_code":"JO","type":"education","lineage":["https://openalex.org/I230091363"]}],"countries":["JO"],"is_corresponding":false,"raw_author_name":"Sahar Qaadan","raw_affiliation_strings":["Jordanian University,Mechatronics Engineering German,Amman,Jordan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Jordanian University,Mechatronics Engineering German,Amman,Jordan","institution_ids":["https://openalex.org/I230091363"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029787567","display_name":"Rami Alazrai","orcid":"https://orcid.org/0000-0002-1296-0231"},"institutions":[{"id":"https://openalex.org/I179311214","display_name":"Gulf University for Science & Technology","ror":"https://ror.org/04d9rzd67","country_code":"KW","type":"education","lineage":["https://openalex.org/I179311214"]}],"countries":["KW"],"is_corresponding":false,"raw_author_name":"Rami Alazrai","raw_affiliation_strings":["Abdullah Al Salem University,College of Computer and Systems Engineering,Khaldiya,Kuwait"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Abdullah Al Salem University,College of Computer and Systems Engineering,Khaldiya,Kuwait","institution_ids":["https://openalex.org/I179311214"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5059048652","display_name":"Aiman Alshare","orcid":"https://orcid.org/0000-0003-3529-4128"},"institutions":[{"id":"https://openalex.org/I230091363","display_name":"German Jordanian University","ror":"https://ror.org/02jgpyd84","country_code":"JO","type":"education","lineage":["https://openalex.org/I230091363"]}],"countries":["JO"],"is_corresponding":false,"raw_author_name":"Aiman Alshare","raw_affiliation_strings":["German Jordanian University,Mechanical and Maintenance Engineering,Amman,Jordan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"German Jordanian University,Mechanical and Maintenance Engineering,Amman,Jordan","institution_ids":["https://openalex.org/I230091363"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.53757313,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"416","last_page":"422"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10663","display_name":"Advanced Battery Technologies Research","score":0.9926000237464905,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive 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/T10663","display_name":"Advanced Battery Technologies Research","score":0.9926000237464905,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive 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/T12127","display_name":"Software System Performance and Reliability","score":0.0010000000474974513,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T10780","display_name":"Reliability and Maintenance Optimization","score":0.0008999999845400453,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"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/weibull-distribution","display_name":"Weibull distribution","score":0.616100013256073},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.6032000184059143},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.5759000182151794},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.564300000667572},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.5587999820709229},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.5354999899864197},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.46650001406669617},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.43529999256134033},{"id":"https://openalex.org/keywords/fault-detection-and-isolation","display_name":"Fault detection and isolation","score":0.4198000133037567}],"concepts":[{"id":"https://openalex.org/C173291955","wikidata":"https://www.wikidata.org/wiki/Q732332","display_name":"Weibull distribution","level":2,"score":0.616100013256073},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.6032000184059143},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5835000276565552},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.5759000182151794},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.564300000667572},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.5587999820709229},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.5354999899864197},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.46650001406669617},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.43529999256134033},{"id":"https://openalex.org/C152745839","wikidata":"https://www.wikidata.org/wiki/Q5438153","display_name":"Fault detection and isolation","level":3,"score":0.4198000133037567},{"id":"https://openalex.org/C200601418","wikidata":"https://www.wikidata.org/wiki/Q2193887","display_name":"Reliability engineering","level":1,"score":0.41499999165534973},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4133000075817108},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.41200000047683716},{"id":"https://openalex.org/C179717631","wikidata":"https://www.wikidata.org/wiki/Q2991667","display_name":"Multilayer perceptron","level":3,"score":0.41190001368522644},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.4081000089645386},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.39500001072883606},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.39469999074935913},{"id":"https://openalex.org/C132459708","wikidata":"https://www.wikidata.org/wiki/Q744069","display_name":"Extrapolation","level":2,"score":0.3783999979496002},{"id":"https://openalex.org/C50897621","wikidata":"https://www.wikidata.org/wiki/Q2665508","display_name":"Hybrid system","level":2,"score":0.3732999861240387},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.37070000171661377},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.3659000098705292},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3594000041484833},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.35690000653266907},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.3176000118255615},{"id":"https://openalex.org/C555008776","wikidata":"https://www.wikidata.org/wiki/Q267298","display_name":"Battery (electricity)","level":3,"score":0.2955999970436096},{"id":"https://openalex.org/C87007009","wikidata":"https://www.wikidata.org/wiki/Q210832","display_name":"Statistical hypothesis testing","level":2,"score":0.29030001163482666},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.2766999900341034},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.274399995803833},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.265500009059906},{"id":"https://openalex.org/C134342201","wikidata":"https://www.wikidata.org/wiki/Q7246859","display_name":"Probabilistic neural network","level":4,"score":0.2581000030040741},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.2556000053882599}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iccma67641.2025.11369643","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccma67641.2025.11369643","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 13th International Conference on Control, Mechatronics and Automation (ICCMA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W188199274","https://openalex.org/W1976526581","https://openalex.org/W2109553965","https://openalex.org/W2157825442","https://openalex.org/W2158698691","https://openalex.org/W2166618387","https://openalex.org/W2296719434","https://openalex.org/W2486038092","https://openalex.org/W2899283552","https://openalex.org/W2924382816","https://openalex.org/W2973897293","https://openalex.org/W3139510216","https://openalex.org/W4200415345","https://openalex.org/W4398164319","https://openalex.org/W4406940415","https://openalex.org/W4410901203","https://openalex.org/W4412130175","https://openalex.org/W4412345840","https://openalex.org/W4412940788"],"related_works":[],"abstract_inverted_index":{"Reliable":[0],"detection":[1,55,180],"of":[2,88,187,192],"early":[3],"anomalies":[4,27],"in":[5,178],"lithium-ion":[6,89,134],"batteries":[7],"is":[8],"essential":[9],"for":[10],"ensuring":[11,79],"safety,":[12],"preventing":[13],"catastrophic":[14],"failures,":[15],"and":[16,22,39,108,122,138,164,182,189],"extending":[17],"operational":[18],"lifetime.":[19],"Conventional":[20],"data-driven":[21],"statistical":[23,109],"approaches":[24],"often":[25],"treat":[26],"as":[28],"isolated":[29],"outliers,":[30],"neglecting":[31],"the":[32,85,93,97,111,131,172,203],"underlying":[33],"physical":[34,77,106],"dependencies":[35],"among":[36],"voltage,":[37],"current,":[38],"temperature":[40],"that":[41,57,80,117,171],"govern":[42],"electrochemical":[43],"dynamics.":[44],"To":[45],"address":[46],"this":[47,49],"limitation,":[48],"paper":[50],"introduces":[51],"a":[52,59,65,145,158,198],"hybrid":[53,112,204],"anomaly":[54,179],"framework":[56,113],"integrates":[58],"Physics-Informed":[60],"Neural":[61],"Network":[62],"(PINN)":[63],"with":[64,84,184],"Weibull":[66,94,147],"reliability":[67],"model.":[68],"The":[69,125,168],"PINN":[70],"component":[71,95],"learns":[72],"discharge":[73],"behavior":[74],"under":[75],"embedded":[76],"constraints,":[78],"predictions":[81],"remain":[82],"consistent":[83],"governing":[86],"equations":[87],"cell":[90],"dynamics,":[91],"while":[92,216],"characterizes":[96],"probabilistic":[98],"degradation":[99,210],"distribution":[100],"across":[101,220],"cycles.":[102],"By":[103],"jointly":[104],"modeling":[105],"consistency":[107],"reliability,":[110],"effectively":[114],"flags":[115],"behaviors":[116],"are":[118],"both":[119],"physically":[120],"implausible":[121],"statistically":[123],"improbable.":[124],"proposed":[126],"approach":[127],"was":[128],"validated":[129],"using":[130],"NASA":[132],"PCoE":[133],"battery":[135],"aging":[136],"dataset":[137],"compared":[139],"against":[140],"multiple":[141],"baseline":[142,195],"models,":[143],"including":[144],"decoupled":[146],"Accelerated":[148],"Failure":[149],"Time":[150],"(AFT)":[151],"model,":[152],"Isolation":[153],"Forest,":[154],"Autoencoder":[155],"based":[156],"on":[157],"Multi-Layer":[159],"Perceptron":[160],"(AE-MLP),":[161],"LSTM":[162],"Autoencoder,":[163],"PINN-only":[165],"residual":[166],"analysis.":[167],"results":[169],"demonstrate":[170],"Hybrid":[173],"Weibull\u2013PINN":[174],"achieves":[175],"substantial":[176],"improvements":[177],"accuracy":[181],"robustness,":[183],"an":[185,190],"AUROC":[186],"0.863":[188],"AUPRC":[191],"0.783\u2014surpassing":[193],"all":[194],"models":[196],"by":[197],"wide":[199],"margin.":[200],"In":[201],"addition,":[202],"method":[205],"exhibits":[206],"early-warning":[207],"capability,":[208],"detecting":[209],"approximately":[211],"40\u201350":[212],"cycles":[213],"before":[214],"failure,":[215],"maintaining":[217],"stable":[218],"performance":[219],"different":[221],"window":[222],"lengths.":[223]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-02-07T00:00:00"}
