{"id":"https://openalex.org/W3147106794","doi":"https://doi.org/10.1109/icmre51691.2021.9384841","title":"Deep &amp; Attention: A Self-Attention based Neural Network for Remaining Useful Lifetime Predictions","display_name":"Deep &amp; Attention: A Self-Attention based Neural Network for Remaining Useful Lifetime Predictions","publication_year":2021,"publication_date":"2021-02-03","ids":{"openalex":"https://openalex.org/W3147106794","doi":"https://doi.org/10.1109/icmre51691.2021.9384841","mag":"3147106794"},"language":"en","primary_location":{"id":"doi:10.1109/icmre51691.2021.9384841","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmre51691.2021.9384841","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 7th International Conference on Mechatronics and Robotics Engineering (ICMRE)","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/A5100624280","display_name":"Yuanjun Liu","orcid":"https://orcid.org/0000-0002-7323-8832"},"institutions":[{"id":"https://openalex.org/I4210112150","display_name":"Institute of Automation","ror":"https://ror.org/022c3hy66","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210112150"]},{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuanjun Liu","raw_affiliation_strings":["Institute of Automation, Chinese Academy of Sciences, School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Automation, Chinese Academy of Sciences, School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210112150","https://openalex.org/I4210165038"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5076823785","display_name":"Xingang Wang","orcid":"https://orcid.org/0000-0002-8665-7024"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210094879","display_name":"Shandong Institute of Automation","ror":"https://ror.org/00qdtba35","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210094879","https://openalex.org/I4210142748"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xingang Wang","raw_affiliation_strings":["Institute of Automation, Chinese Academy of Sciences, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Automation, Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210094879","https://openalex.org/I19820366"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":17,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"98","last_page":"105"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.9979000091552734,"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":0.9979000091552734,"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":0.9911999702453613,"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/T11918","display_name":"Forecasting Techniques and Applications","score":0.9574999809265137,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/prognostics","display_name":"Prognostics","score":0.8791427612304688},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7458496689796448},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.7068135738372803},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6101268529891968},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6075910925865173},{"id":"https://openalex.org/keywords/modular-design","display_name":"Modular design","score":0.5975200533866882},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5445979833602905},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.5352429151535034},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5119298696517944},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.47670978307724},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.43859827518463135},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.40416544675827026},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.26730334758758545},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.0821121335029602}],"concepts":[{"id":"https://openalex.org/C129364497","wikidata":"https://www.wikidata.org/wiki/Q3042561","display_name":"Prognostics","level":2,"score":0.8791427612304688},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7458496689796448},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.7068135738372803},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6101268529891968},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6075910925865173},{"id":"https://openalex.org/C101468663","wikidata":"https://www.wikidata.org/wiki/Q1620158","display_name":"Modular design","level":2,"score":0.5975200533866882},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5445979833602905},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.5352429151535034},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5119298696517944},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.47670978307724},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.43859827518463135},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.40416544675827026},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.26730334758758545},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0821121335029602},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icmre51691.2021.9384841","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmre51691.2021.9384841","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 7th International Conference on Mechatronics and Robotics Engineering (ICMRE)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G6033411719","display_name":null,"funder_award_id":"2018YFD0400902","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"}],"funders":[{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":42,"referenced_works":["https://openalex.org/W1972411326","https://openalex.org/W1978965153","https://openalex.org/W2030166992","https://openalex.org/W2044309218","https://openalex.org/W2054570131","https://openalex.org/W2064675550","https://openalex.org/W2107776973","https://openalex.org/W2107878631","https://openalex.org/W2120841219","https://openalex.org/W2147800946","https://openalex.org/W2157331557","https://openalex.org/W2160815625","https://openalex.org/W2194775991","https://openalex.org/W2206858481","https://openalex.org/W2415594836","https://openalex.org/W2471161958","https://openalex.org/W2558869916","https://openalex.org/W2593657034","https://openalex.org/W2604168140","https://openalex.org/W2606754133","https://openalex.org/W2617137613","https://openalex.org/W2618530766","https://openalex.org/W2744067593","https://openalex.org/W2747276445","https://openalex.org/W2772084711","https://openalex.org/W2792286928","https://openalex.org/W2898735569","https://openalex.org/W2900438754","https://openalex.org/W2902700103","https://openalex.org/W2912279724","https://openalex.org/W2963403868","https://openalex.org/W2963532813","https://openalex.org/W2963542991","https://openalex.org/W3012642762","https://openalex.org/W4385245566","https://openalex.org/W6629368666","https://openalex.org/W6720561750","https://openalex.org/W6735477854","https://openalex.org/W6739901393","https://openalex.org/W6745408861","https://openalex.org/W6749667008","https://openalex.org/W6758938078"],"related_works":["https://openalex.org/W2310476526","https://openalex.org/W3213192587","https://openalex.org/W2144291498","https://openalex.org/W2535730979","https://openalex.org/W2030958945","https://openalex.org/W2370073012","https://openalex.org/W2168646784","https://openalex.org/W4386567722","https://openalex.org/W2466930957","https://openalex.org/W3182014137"],"abstract_inverted_index":{"The":[0],"remaining":[1],"useful":[2],"lifetime":[3],"(RUL)":[4],"of":[5,44,52,56,77,85,108,129,134],"assets":[6],"plays":[7],"a":[8,75,127],"critical":[9],"role":[10],"in":[11],"machine":[12],"prognostics":[13],"and":[14,48,81],"health":[15],"management":[16],"(PHM).":[17],"Accurate":[18],"RUL":[19,137],"predictions":[20],"can":[21],"reduce":[22],"losses":[23],"caused":[24],"by":[25],"equipment":[26],"faults.":[27],"Most":[28],"existing":[29],"data-driven":[30],"PHM":[31],"methods":[32],"rely":[33],"on":[34,112],"long":[35],"short-term":[36],"memory":[37],"(LSTM)":[38],"networks":[39,80],"to":[40,62,132,150],"model":[41],"the":[42,53,68,88,94,98,102,106,123],"relationship":[43],"time":[45],"series":[46],"data":[47],"RUL.":[49],"However,":[50],"because":[51],"sequential":[54],"nature":[55],"LSTM,":[57],"it":[58],"is":[59,148],"not":[60],"conducive":[61,149],"parallel":[63,151],"computing.":[64,152],"Herein,":[65],"we":[66],"propose":[67],"Deep":[69,90,103],"&":[70,91],"Attention":[71,82,92,95],"Network,":[72,93],"which":[73],"uses":[74],"combination":[76],"convolutional":[78],"neural":[79],"methodologies":[83],"instead":[84],"LSTM.":[86],"In":[87],"proposed":[89,124],"component":[96,104],"models":[97],"temporal":[99],"property,":[100],"while":[101],"learns":[105],"effect":[107],"noise":[109],"data.":[110],"Experiments":[111],"NASA's":[113],"Commercial":[114],"Modular":[115],"Aero-":[116],"Propulsion":[117],"System":[118],"Simulation":[119],"datasets":[120],"demonstrate":[121],"that":[122,133],"network":[125],"achieves":[126],"level":[128],"performance":[130],"similar":[131],"other":[135],"state-of-the-art":[136],"prediction":[138],"models.":[139],"Moreover,":[140],"compared":[141],"with":[142],"LSTM-based":[143],"methods,":[144],"our":[145],"Self-Attention-based":[146],"method":[147]},"counts_by_year":[{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":7}],"updated_date":"2026-07-15T18:14:33.161393","created_date":"2025-10-10T00:00:00"}
