{"id":"https://openalex.org/W7134133039","doi":"https://doi.org/10.1109/access.2026.3671697","title":"Integrated Diagnosis and Prognosis of Dynamic Systems Using Deep Learning: Case Study on a Welding Robot","display_name":"Integrated Diagnosis and Prognosis of Dynamic Systems Using Deep Learning: Case Study on a Welding Robot","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7134133039","doi":"https://doi.org/10.1109/access.2026.3671697"},"language":"en","primary_location":{"id":"doi:10.1109/access.2026.3671697","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3671697","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2026.3671697","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5000865751","display_name":"Aslain Brisco Ngnassi Djami","orcid":"https://orcid.org/0000-0002-4524-5242"},"institutions":[{"id":"https://openalex.org/I117723502","display_name":"University of N'Djamena","ror":"https://ror.org/013gpqv08","country_code":"TD","type":"education","lineage":["https://openalex.org/I117723502"]}],"countries":["TD"],"is_corresponding":false,"raw_author_name":"Aslain Brisco Ngnassi Djami","raw_affiliation_strings":["Study and Research Laboratory in Industrial Techniques, University of N&#x2019;Djamena, N&#x2019;Djamena, Chad"],"raw_orcid":"https://orcid.org/0000-0002-4524-5242","affiliations":[{"raw_affiliation_string":"Study and Research Laboratory in Industrial Techniques, University of N&#x2019;Djamena, N&#x2019;Djamena, Chad","institution_ids":["https://openalex.org/I117723502"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5043147014","display_name":"Boukar Abdelhakim","orcid":null},"institutions":[{"id":"https://openalex.org/I117723502","display_name":"University of N'Djamena","ror":"https://ror.org/013gpqv08","country_code":"TD","type":"education","lineage":["https://openalex.org/I117723502"]}],"countries":["TD"],"is_corresponding":false,"raw_author_name":"Boukar Abdelhakim","raw_affiliation_strings":["Study and Research Laboratory in Industrial Techniques, University of N&#x2019;Djamena, N&#x2019;Djamena, Chad"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Study and Research Laboratory in Industrial Techniques, University of N&#x2019;Djamena, N&#x2019;Djamena, Chad","institution_ids":["https://openalex.org/I117723502"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I117723502"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":15.6902,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.98808289,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":98},"biblio":{"volume":"14","issue":null,"first_page":"36737","last_page":"36757"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.7013000249862671,"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":0.7013000249862671,"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":0.061000000685453415,"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":0.03849999979138374,"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/interpretability","display_name":"Interpretability","score":0.8830000162124634},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5464000105857849},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.4713999927043915},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.44359999895095825},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.43050000071525574},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.41179999709129333},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.40540000796318054},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.40070000290870667},{"id":"https://openalex.org/keywords/welding","display_name":"Welding","score":0.39149999618530273},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.36059999465942383}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.8830000162124634},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7836999893188477},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6378999948501587},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5464000105857849},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5250999927520752},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4713999927043915},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.44359999895095825},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.43050000071525574},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.41179999709129333},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.40540000796318054},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.40070000290870667},{"id":"https://openalex.org/C19474535","wikidata":"https://www.wikidata.org/wiki/Q131172","display_name":"Welding","level":2,"score":0.39149999618530273},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.36059999465942383},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3499999940395355},{"id":"https://openalex.org/C175551986","wikidata":"https://www.wikidata.org/wiki/Q47089","display_name":"Fault (geology)","level":2,"score":0.34200000762939453},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.34150001406669617},{"id":"https://openalex.org/C152745839","wikidata":"https://www.wikidata.org/wiki/Q5438153","display_name":"Fault detection and isolation","level":3,"score":0.33880001306533813},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3375999927520752},{"id":"https://openalex.org/C47702885","wikidata":"https://www.wikidata.org/wiki/Q5441227","display_name":"Feedforward neural network","level":3,"score":0.3257000148296356},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.32109999656677246},{"id":"https://openalex.org/C2776999362","wikidata":"https://www.wikidata.org/wiki/Q2349274","display_name":"Planner","level":2,"score":0.3021000027656555},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.29170000553131104},{"id":"https://openalex.org/C2775846686","wikidata":"https://www.wikidata.org/wiki/Q643012","display_name":"Condition monitoring","level":2,"score":0.28600001335144043},{"id":"https://openalex.org/C34413123","wikidata":"https://www.wikidata.org/wiki/Q170978","display_name":"Robotics","level":3,"score":0.28220000863075256},{"id":"https://openalex.org/C63540848","wikidata":"https://www.wikidata.org/wiki/Q3140932","display_name":"Fault tolerance","level":2,"score":0.2750999927520752},{"id":"https://openalex.org/C19966478","wikidata":"https://www.wikidata.org/wiki/Q4810574","display_name":"Mobile robot","level":3,"score":0.2711000144481659},{"id":"https://openalex.org/C38858127","wikidata":"https://www.wikidata.org/wiki/Q5441228","display_name":"Feed forward","level":2,"score":0.26899999380111694},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.26840001344680786},{"id":"https://openalex.org/C89611455","wikidata":"https://www.wikidata.org/wiki/Q6804646","display_name":"Mechanism (biology)","level":2,"score":0.25870001316070557},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.25130000710487366},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.25119999051094055}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2026.3671697","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3671697","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:b3dff91e7bea451d9542b13b1e77f7cc","is_oa":true,"landing_page_url":"https://doaj.org/article/b3dff91e7bea451d9542b13b1e77f7cc","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 14, Pp 36737-36757 (2026)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2026.3671697","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3671697","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","score":0.4548640251159668,"id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":92,"referenced_works":["https://openalex.org/W1554187497","https://openalex.org/W1654016072","https://openalex.org/W1689711448","https://openalex.org/W1899504021","https://openalex.org/W1966547590","https://openalex.org/W2000805819","https://openalex.org/W2006169073","https://openalex.org/W2016210396","https://openalex.org/W2020362991","https://openalex.org/W2033800551","https://openalex.org/W2037183236","https://openalex.org/W2042883160","https://openalex.org/W2047539961","https://openalex.org/W2058580716","https://openalex.org/W2064323378","https://openalex.org/W2064675550","https://openalex.org/W2085862958","https://openalex.org/W2096947102","https://openalex.org/W2107878631","https://openalex.org/W2110221383","https://openalex.org/W2110787940","https://openalex.org/W2122646361","https://openalex.org/W2136848157","https://openalex.org/W2143612262","https://openalex.org/W2147129131","https://openalex.org/W2149956719","https://openalex.org/W2155832827","https://openalex.org/W2157331557","https://openalex.org/W2170505850","https://openalex.org/W2293634267","https://openalex.org/W2471161958","https://openalex.org/W2505576420","https://openalex.org/W2516809705","https://openalex.org/W2580840020","https://openalex.org/W2585502432","https://openalex.org/W2593519057","https://openalex.org/W2612554669","https://openalex.org/W2613328025","https://openalex.org/W2617137613","https://openalex.org/W2625625371","https://openalex.org/W2754252319","https://openalex.org/W2773549135","https://openalex.org/W2786827964","https://openalex.org/W2790625295","https://openalex.org/W2810292802","https://openalex.org/W2885578090","https://openalex.org/W2901072570","https://openalex.org/W2919115771","https://openalex.org/W2954996726","https://openalex.org/W2963121266","https://openalex.org/W2963847595","https://openalex.org/W2964010366","https://openalex.org/W2986110703","https://openalex.org/W2990030370","https://openalex.org/W3022112205","https://openalex.org/W3118516021","https://openalex.org/W3126230403","https://openalex.org/W3130237369","https://openalex.org/W3134550335","https://openalex.org/W3138819813","https://openalex.org/W3142292878","https://openalex.org/W3146551586","https://openalex.org/W3163993681","https://openalex.org/W3164155163","https://openalex.org/W3166238888","https://openalex.org/W3167954679","https://openalex.org/W3169571897","https://openalex.org/W3170344786","https://openalex.org/W3177318507","https://openalex.org/W3197233787","https://openalex.org/W3211281790","https://openalex.org/W4211049957","https://openalex.org/W4224220194","https://openalex.org/W4225546882","https://openalex.org/W4234451427","https://openalex.org/W4249224151","https://openalex.org/W4250143236","https://openalex.org/W4254913624","https://openalex.org/W4292182793","https://openalex.org/W4295330333","https://openalex.org/W4383097042","https://openalex.org/W4385245566","https://openalex.org/W4385357628","https://openalex.org/W4387343609","https://openalex.org/W4388773795","https://openalex.org/W4388774242","https://openalex.org/W4392120293","https://openalex.org/W4399896567","https://openalex.org/W4400101098","https://openalex.org/W4403731244","https://openalex.org/W4403996473","https://openalex.org/W4407276595"],"related_works":[],"abstract_inverted_index":{"This":[0],"study":[1],"addresses":[2],"the":[3,67,92],"challenges":[4],"of":[5,139,162],"diagnosis":[6],"and":[7,32,49,71,84,91,110,118,127,136,181,186],"prognosis":[8],"in":[9,124,158,192],"complex":[10],"dynamic":[11],"systems,":[12],"focusing":[13],"on":[14,25],"an":[15],"industrial":[16,193],"MIG/MAG":[17],"robotic":[18,194],"welding":[19],"application.":[20],"A":[21,78],"robust":[22],"framework":[23,174],"based":[24],"Recurrent":[26],"Neural":[27],"Networks":[28],"(RNNs),":[29],"specifically":[30],"LSTM":[31,119],"GRU":[33,117],"architectures,":[34],"was":[35,89],"developed":[36],"to":[37,62,75,132],"analyze":[38],"multivariate":[39],"time-series":[40],"data":[41],"from":[42],"real-world":[43],"sensor":[44],"measurements":[45],"for":[46,147,168,189],"fault":[47,163],"detection":[48],"remaining":[50],"useful":[51],"life":[52],"(RUL)":[53],"prediction.":[54,77],"The":[55,150,171],"approach":[56],"incorporates":[57],"a":[58,85,184],"post-hoc":[59],"attention":[60,151],"mechanism":[61,152],"enhance":[63],"interpretability":[64],"by":[65],"identifying":[66],"most":[68],"influential":[69],"variables":[70],"time":[72],"windows":[73],"contributing":[74],"each":[76],"sliding-window":[79],"method":[80],"(50":[81],"historical":[82],"steps":[83],"10-step":[86],"prediction":[87],"horizon)":[88],"employed,":[90],"models":[93,120],"were":[94],"evaluated":[95],"against":[96],"various":[97],"benchmarks,":[98],"including":[99],"decision":[100],"trees,":[101],"feedforward":[102],"neural":[103],"networks,":[104],"random":[105],"forests,":[106],"1D":[107],"CNNs,":[108],"CNN\u2013LSTM,":[109],"transformer":[111],"architectures.":[112],"Experimental":[113],"results":[114],"demonstrate":[115],"that":[116],"outperform":[121],"all":[122],"baselines":[123],"both":[125],"diagnostic":[126,134],"prognostic":[128],"tasks,":[129],"achieving":[130],"up":[131],"94%":[133],"accuracy":[135],"inference":[137],"times":[138],"less":[140],"than":[141,160],"100":[142],"ms,":[143],"making":[144],"them":[145],"suitable":[146],"real-time":[148,179],"deployment.":[149],"consistently":[153],"identified":[154],"key":[155],"degradation":[156],"signatures":[157],"more":[159],"85%":[161],"sequences,":[164],"offering":[165],"valuable":[166],"insights":[167],"domain":[169],"experts.":[170],"proposed":[172],"RNN-based":[173],"therefore":[175],"combines":[176],"predictive":[177],"accuracy,":[178],"performance,":[180],"interpretability,":[182],"providing":[183],"scalable":[185],"effective":[187],"solution":[188],"intelligent":[190],"maintenance":[191],"systems.":[195]},"counts_by_year":[{"year":2026,"cited_by_count":2}],"updated_date":"2026-06-30T13:55:48.251075","created_date":"2026-03-08T00:00:00"}
