{"id":"https://openalex.org/W7135102633","doi":"https://doi.org/10.1109/icsrs68021.2025.11422078","title":"Temporal Sequence Modeling for Rare Failure Prediction in Industrial Machinery Using a Hybrid CNN-LSTM Model","display_name":"Temporal Sequence Modeling for Rare Failure Prediction in Industrial Machinery Using a Hybrid CNN-LSTM Model","publication_year":2025,"publication_date":"2025-11-26","ids":{"openalex":"https://openalex.org/W7135102633","doi":"https://doi.org/10.1109/icsrs68021.2025.11422078"},"language":null,"primary_location":{"id":"doi:10.1109/icsrs68021.2025.11422078","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icsrs68021.2025.11422078","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 9th International Conference on System Reliability and Safety (ICSRS)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5091079141","display_name":"Mehdi Faraz","orcid":"https://orcid.org/0000-0002-8719-6477"},"institutions":[{"id":"https://openalex.org/I155660961","display_name":"University of Turku","ror":"https://ror.org/05vghhr25","country_code":"FI","type":"education","lineage":["https://openalex.org/I155660961"]}],"countries":["FI"],"is_corresponding":false,"raw_author_name":"Mehdi Faraz","raw_affiliation_strings":["University of Turku,Department of Computing,Turku,Finland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Turku,Department of Computing,Turku,Finland","institution_ids":["https://openalex.org/I155660961"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018523000","display_name":"Viktoriia Shubina","orcid":"https://orcid.org/0000-0001-8178-5652"},"institutions":[{"id":"https://openalex.org/I155660961","display_name":"University of Turku","ror":"https://ror.org/05vghhr25","country_code":"FI","type":"education","lineage":["https://openalex.org/I155660961"]}],"countries":["FI"],"is_corresponding":false,"raw_author_name":"Viktoriia Shubina","raw_affiliation_strings":["University of Turku,Department of Computing,Turku,Finland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Turku,Department of Computing,Turku,Finland","institution_ids":["https://openalex.org/I155660961"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000829089","display_name":"Tuomas M\u00e4kil\u00e4","orcid":"https://orcid.org/0000-0002-8799-185X"},"institutions":[{"id":"https://openalex.org/I155660961","display_name":"University of Turku","ror":"https://ror.org/05vghhr25","country_code":"FI","type":"education","lineage":["https://openalex.org/I155660961"]}],"countries":["FI"],"is_corresponding":false,"raw_author_name":"Tuomas M\u00e4kil\u00e4","raw_affiliation_strings":["University of Turku,Department of Computing,Turku,Finland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Turku,Department of Computing,Turku,Finland","institution_ids":["https://openalex.org/I155660961"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5058539829","display_name":"Jukka Heikkonen","orcid":"https://orcid.org/0000-0002-2468-5708"},"institutions":[{"id":"https://openalex.org/I155660961","display_name":"University of Turku","ror":"https://ror.org/05vghhr25","country_code":"FI","type":"education","lineage":["https://openalex.org/I155660961"]}],"countries":["FI"],"is_corresponding":false,"raw_author_name":"Jukka Heikkonen","raw_affiliation_strings":["University of Turku,Department of Computing,Turku,Finland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Turku,Department of Computing,Turku,Finland","institution_ids":["https://openalex.org/I155660961"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I155660961"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.76021672,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"277","last_page":"286"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.8726999759674072,"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":0.8726999759674072,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.0357000008225441,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.03500000014901161,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.6385999917984009},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.5620999932289124},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5543000102043152},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.49149999022483826},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4814000129699707},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4787999987602234},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.46299999952316284}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6958000063896179},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.6385999917984009},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5916000008583069},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.5620999932289124},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5543000102043152},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.49149999022483826},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4814000129699707},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4787999987602234},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.46299999952316284},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.4586000144481659},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.45249998569488525},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.38609999418258667},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3625999987125397},{"id":"https://openalex.org/C2777317252","wikidata":"https://www.wikidata.org/wiki/Q18393516","display_name":"Rare events","level":2,"score":0.35499998927116394},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.35499998927116394},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.33739998936653137},{"id":"https://openalex.org/C2780150128","wikidata":"https://www.wikidata.org/wiki/Q21948731","display_name":"Extreme learning machine","level":3,"score":0.2881999909877777},{"id":"https://openalex.org/C152745839","wikidata":"https://www.wikidata.org/wiki/Q5438153","display_name":"Fault detection and isolation","level":3,"score":0.25189998745918274},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.25119999051094055}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icsrs68021.2025.11422078","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icsrs68021.2025.11422078","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 9th International Conference on System Reliability and Safety (ICSRS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.42790964245796204}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W65738273","https://openalex.org/W1995443851","https://openalex.org/W2001619934","https://openalex.org/W2008972967","https://openalex.org/W2064675550","https://openalex.org/W2100495367","https://openalex.org/W2112796928","https://openalex.org/W2118978333","https://openalex.org/W2132870739","https://openalex.org/W2177899970","https://openalex.org/W2222570178","https://openalex.org/W2295598076","https://openalex.org/W2601590138","https://openalex.org/W2805611890","https://openalex.org/W2943859586","https://openalex.org/W2972137370","https://openalex.org/W2998506103","https://openalex.org/W3016420721","https://openalex.org/W3016838260","https://openalex.org/W3043609702","https://openalex.org/W3101503122","https://openalex.org/W3180061397","https://openalex.org/W3184853819","https://openalex.org/W4285212650","https://openalex.org/W4378804743","https://openalex.org/W4388283660","https://openalex.org/W4390552584","https://openalex.org/W4400762160"],"related_works":[],"abstract_inverted_index":{"Predictive":[0],"maintenance":[1],"is":[2],"vital":[3],"for":[4,71,78,85,108,253,264],"enhancing":[5],"industrial":[6,260],"machinery":[7],"reliability":[8,261],"by":[9,235],"detecting":[10,163],"rare":[11,50,148,232],"failures":[12,51],"in":[13,259],"high-dimensional,":[14],"imbalanced":[15,109],"sensor":[16],"data.":[17],"This":[18],"study":[19],"analyzes":[20],"a":[21,62,103,111],"dataset":[22],"of":[23,115,147,166,247],"220320":[24],"samples,":[25],"with":[26,170,251,267],"failure":[27,133,168,233],"events":[28],"(BROKEN)":[29],"constituting":[30],"only":[31],"0.003%.":[32],"Initial":[33],"state":[34,116],"classification":[35],"using":[36,139],"autoencoders":[37],"and":[38,82,120,135,152,220,239,262],"ensemble":[39],"classifiers":[40],"(Random":[41],"Forest,":[42,125],"Extreme":[43],"Gradient":[44],"Boosting)":[45],"failed":[46],"to":[47,53,143,178,182],"detect":[48],"these":[49],"due":[52,177],"extreme":[54,268],"class":[55,269],"imbalance.":[56,270],"To":[57],"address":[58],"this,":[59],"we":[60],"developed":[61],"hybrid":[63,156,228],"Convolutional":[64],"Neural":[65],"Network-Long":[66],"Short-Term":[67],"Memory":[68],"(CNN-LSTM)":[69],"model":[70,158,229],"temporal":[72,87,118,132,240],"sequence":[73],"modeling,":[74,119],"utilizing":[75],"convolutional":[76],"layers":[77,84],"spatial":[79,238],"feature":[80,105],"extraction":[81],"LSTM":[83,194],"capturing":[86,237],"dependencies":[88],"within":[89],"6":[90,164],"-sample":[91],"windows,":[92],"classified":[93],"as":[94],"Normal":[95],"or":[96],"Transient":[97],"(failure":[98],"precursors).":[99],"Main":[100],"contributions":[101],"include":[102],"robust":[104,145],"selection":[106],"method":[107],"datasets,":[110],"comprehensive":[112],"comparative":[113],"analysis":[114],"classification,":[117],"unsupervised":[121],"anomaly":[122],"detection":[123,234],"(Isolation":[124],"One-Class":[126,221],"Support":[127],"Vector":[128],"Machine),":[129],"insights":[130],"into":[131],"precursors,":[134],"an":[136],"evaluation":[137],"framework":[138],"Leave-One-Out":[140],"Cross-Validation":[141],"(LOOCV)":[142],"ensure":[144],"assessment":[146],"events,":[149],"balancing":[150],"accuracy":[151],"early":[153],"detection.":[154],"The":[155],"CNN-LSTM":[157,249],"achieved":[159],"97.88%":[160],"overall":[161],"accuracy,":[162],"out":[165],"7":[167,203],"cases":[169],"85.71%":[171],"recall,":[172],"though":[173],"precision":[174],"was":[175],"30%":[176],"false":[179],"positives.":[180],"Compared":[181],"baseline":[183],"CNN":[184],"$(4.82":[185],"\\%$":[186],"precision,":[187,199,213,224],"$\\mathbf{5":[188],"7.":[189],"1":[190],"4":[191],"\\%}$":[192,198,204,212,218],"recall),":[193,205,219,226],"($\\mathbf{1.":[195],"0":[196,210,211],"2":[197,216],"$\\mathbf{2":[200],"8.":[201],"5":[202],"Isolation":[206],"Forest":[207],"($\\mathbf{5":[208],"0.":[209],"$\\mathbf{1":[214],"4.":[215],"9":[217],"SVM":[222],"(8.00%":[223],"28.57%":[225],"the":[227,245,248],"significantly":[230],"improved":[231],"effectively":[236],"patterns.":[241],"These":[242],"results":[243],"highlight":[244],"efficacy":[246],"approach":[250],"LOOCV":[252],"proactive":[254],"maintenance,":[255],"offering":[256],"substantial":[257],"improvements":[258],"safety":[263],"real-world":[265],"applications":[266]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2026-03-13T00:00:00"}