{"id":"https://openalex.org/W2954634015","doi":"https://doi.org/10.1109/icit.2019.8755209","title":"A LS-SVM based Approach for Turbine Engines Prognostics Using Sensor Data","display_name":"A LS-SVM based Approach for Turbine Engines Prognostics Using Sensor Data","publication_year":2019,"publication_date":"2019-02-01","ids":{"openalex":"https://openalex.org/W2954634015","doi":"https://doi.org/10.1109/icit.2019.8755209","mag":"2954634015"},"language":"en","primary_location":{"id":"doi:10.1109/icit.2019.8755209","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icit.2019.8755209","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Industrial Technology (ICIT)","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/A5111727923","display_name":"Yongxiang Li","orcid":null},"institutions":[{"id":"https://openalex.org/I4210086028","display_name":"Technology and Engineering Center for Space Utilization","ror":"https://ror.org/00cn03n83","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210086028"]},{"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":true,"raw_author_name":"Yongxiang Li","raw_affiliation_strings":["Technology and Engineering Center for Space Utilization, University of Chinese Academy of Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Technology and Engineering Center for Space Utilization, University of Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210086028","https://openalex.org/I4210165038"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050960188","display_name":"Xiaoming Shan","orcid":null},"institutions":[{"id":"https://openalex.org/I66883779","display_name":"Weifang University","ror":"https://ror.org/01frp7483","country_code":"CN","type":"education","lineage":["https://openalex.org/I66883779"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoming Shan","raw_affiliation_strings":["Weifang International, Information Industry Research Center, Weifang, China"],"affiliations":[{"raw_affiliation_string":"Weifang International, Information Industry Research Center, Weifang, China","institution_ids":["https://openalex.org/I66883779"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005669199","display_name":"Wei Zhao","orcid":"https://orcid.org/0000-0001-7298-238X"},"institutions":[{"id":"https://openalex.org/I4210086028","display_name":"Technology and Engineering Center for Space Utilization","ror":"https://ror.org/00cn03n83","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210086028"]},{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Zhao","raw_affiliation_strings":["Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210086028","https://openalex.org/I19820366"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100635313","display_name":"Gong Wang","orcid":"https://orcid.org/0000-0002-5568-5903"},"institutions":[{"id":"https://openalex.org/I4210086028","display_name":"Technology and Engineering Center for Space Utilization","ror":"https://ror.org/00cn03n83","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210086028"]},{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Gong Wang","raw_affiliation_strings":["Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210086028","https://openalex.org/I19820366"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5111727923"],"corresponding_institution_ids":["https://openalex.org/I4210086028","https://openalex.org/I4210165038"],"apc_list":null,"apc_paid":null,"fwci":0.1261,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.49199221,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"33","issue":null,"first_page":"983","last_page":"987"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13858","display_name":"Technical Engine Diagnostics and Monitoring","score":0.9952999949455261,"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/T13858","display_name":"Technical Engine Diagnostics and Monitoring","score":0.9952999949455261,"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/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.9941999912261963,"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/T14257","display_name":"Advanced Measurement and Detection Methods","score":0.9908999800682068,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/prognostics","display_name":"Prognostics","score":0.9337246417999268},{"id":"https://openalex.org/keywords/turbofan","display_name":"Turbofan","score":0.8801711797714233},{"id":"https://openalex.org/keywords/turbine","display_name":"Turbine","score":0.7482105493545532},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.679200291633606},{"id":"https://openalex.org/keywords/condition-monitoring","display_name":"Condition monitoring","score":0.5444958209991455},{"id":"https://openalex.org/keywords/degradation","display_name":"Degradation (telecommunications)","score":0.5321523547172546},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4662341773509979},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.458067923784256},{"id":"https://openalex.org/keywords/automotive-engineering","display_name":"Automotive engineering","score":0.430361270904541},{"id":"https://openalex.org/keywords/reliability-engineering","display_name":"Reliability engineering","score":0.3569451868534088},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.32568347454071045},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.22021383047103882},{"id":"https://openalex.org/keywords/aerospace-engineering","display_name":"Aerospace engineering","score":0.08145615458488464},{"id":"https://openalex.org/keywords/electronic-engineering","display_name":"Electronic engineering","score":0.0748281180858612}],"concepts":[{"id":"https://openalex.org/C129364497","wikidata":"https://www.wikidata.org/wiki/Q3042561","display_name":"Prognostics","level":2,"score":0.9337246417999268},{"id":"https://openalex.org/C110050840","wikidata":"https://www.wikidata.org/wiki/Q654051","display_name":"Turbofan","level":2,"score":0.8801711797714233},{"id":"https://openalex.org/C2778449969","wikidata":"https://www.wikidata.org/wiki/Q130760","display_name":"Turbine","level":2,"score":0.7482105493545532},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.679200291633606},{"id":"https://openalex.org/C2775846686","wikidata":"https://www.wikidata.org/wiki/Q643012","display_name":"Condition monitoring","level":2,"score":0.5444958209991455},{"id":"https://openalex.org/C2779679103","wikidata":"https://www.wikidata.org/wiki/Q5251805","display_name":"Degradation (telecommunications)","level":2,"score":0.5321523547172546},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4662341773509979},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.458067923784256},{"id":"https://openalex.org/C171146098","wikidata":"https://www.wikidata.org/wiki/Q124192","display_name":"Automotive engineering","level":1,"score":0.430361270904541},{"id":"https://openalex.org/C200601418","wikidata":"https://www.wikidata.org/wiki/Q2193887","display_name":"Reliability engineering","level":1,"score":0.3569451868534088},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.32568347454071045},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.22021383047103882},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.08145615458488464},{"id":"https://openalex.org/C24326235","wikidata":"https://www.wikidata.org/wiki/Q126095","display_name":"Electronic engineering","level":1,"score":0.0748281180858612},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icit.2019.8755209","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icit.2019.8755209","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Industrial Technology (ICIT)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Responsible consumption and production","id":"https://metadata.un.org/sdg/12","score":0.4300000071525574}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W1601740268","https://openalex.org/W2031392614","https://openalex.org/W2084136177","https://openalex.org/W2120841219","https://openalex.org/W2156909104","https://openalex.org/W2510860466","https://openalex.org/W2520420203","https://openalex.org/W2538830880","https://openalex.org/W2762626234","https://openalex.org/W6729063811"],"related_works":["https://openalex.org/W4200169289","https://openalex.org/W2908973203","https://openalex.org/W2801712269","https://openalex.org/W1872896676","https://openalex.org/W2156691445","https://openalex.org/W2045186954","https://openalex.org/W3000986292","https://openalex.org/W2936827077","https://openalex.org/W2741863531","https://openalex.org/W4317418581"],"abstract_inverted_index":{"As":[0,163],"the":[1,6,14,17,22,28,37,63,67,87,109,117,137,144,150,153,160,167,182,185],"core":[2],"part":[3],"of":[4,16,27,74,89,152,184],"aircraft,":[5],"engine's":[7],"health":[8,118],"condition":[9],"is":[10,40,49,105,122,130,178],"tightly":[11],"related":[12],"to":[13,20,56,61,76,85,107,180],"safety":[15],"aircraft.":[18],"Therefore,":[19],"predict":[21],"remaining":[23],"useful":[24],"life":[25,46],"(RUL)":[26],"engine":[29,39,64,69,111,169],"accurately":[30],"has":[31],"a":[32,41,58,72,94,123],"great":[33],"practical":[34],"significance.":[35],"Since":[36],"turbine":[38,68,110],"complex":[42],"system":[43],"and":[44],"its":[45],"degradation":[47,138,161,170],"process":[48],"affected":[50],"by":[51,159,175],"many":[52],"factors,":[53],"it's":[54],"hard":[55],"use":[57],"certain":[59],"model":[60],"represent":[62],"degradation.":[65,90],"Fortunately,":[66],"often":[70],"sets":[71],"variety":[73],"sensors":[75],"obtain":[77],"operational":[78],"data,":[79,129],"which":[80,121],"provides":[81],"an":[82,164],"effective":[83],"way":[84],"monitor":[86],"trend":[88],"In":[91],"this":[92],"paper,":[93],"prognostic":[95],"approach":[96],"based":[97,146],"on":[98,147],"least":[99],"squares":[100],"support":[101],"vector":[102],"machine":[103],"(LS-SVM)":[104],"proposed":[106,186],"estimate":[108],"RUL":[112,151],"using":[113,132],"sensor":[114,128],"data.":[115],"Firstly,":[116],"indicator":[119,125],"(HI),":[120],"synthesis":[124],"from":[126],"multivariate":[127],"calculated":[131,158],"descending":[133],"dimension":[134],"method,":[135],"then":[136],"models":[139],"are":[140],"built":[141],"through":[142],"regressing":[143],"HIs":[145],"LS-SVM.":[148],"Finally,":[149],"testing":[154],"engines":[155],"can":[156],"be":[157],"models.":[162],"application":[165],"case,":[166],"turbofan":[168],"simulation":[171],"data":[172],"set":[173],"supplied":[174],"NASA":[176],"Ames":[177],"used":[179],"demonstrate":[181],"performance":[183],"approach.":[187]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}