{"id":"https://openalex.org/W4200249304","doi":"https://doi.org/10.1145/3487075.3487134","title":"Remaining Useful Life Prediction of Equipment Based on XGBoost","display_name":"Remaining Useful Life Prediction of Equipment Based on XGBoost","publication_year":2021,"publication_date":"2021-10-19","ids":{"openalex":"https://openalex.org/W4200249304","doi":"https://doi.org/10.1145/3487075.3487134"},"language":"en","primary_location":{"id":"doi:10.1145/3487075.3487134","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3487075.3487134","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 5th International Conference on Computer Science and Application Engineering","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/A5014632549","display_name":"Zhiyang Jia","orcid":"https://orcid.org/0000-0003-3248-8875"},"institutions":[{"id":"https://openalex.org/I204553293","display_name":"China University of Petroleum, Beijing","ror":"https://ror.org/041qf4r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I204553293"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zhiyang Jia","raw_affiliation_strings":["Department of Computer Science, China University of Petroleum-Beijing at Karamay, China"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, China University of Petroleum-Beijing at Karamay, China","institution_ids":["https://openalex.org/I204553293"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5106406108","display_name":"Zhibo Xiao","orcid":"https://orcid.org/0000-0002-7245-3186"},"institutions":[{"id":"https://openalex.org/I111599522","display_name":"Jiangnan University","ror":"https://ror.org/04mkzax54","country_code":"CN","type":"education","lineage":["https://openalex.org/I111599522"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhibo xiao","raw_affiliation_strings":["School of Science, Jiangnan University, China"],"affiliations":[{"raw_affiliation_string":"School of Science, Jiangnan University, China","institution_ids":["https://openalex.org/I111599522"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5104101058","display_name":"Yijin Shi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yijin Shi","raw_affiliation_strings":["Department of Information, Lijiang Culture And Tourism College, China"],"affiliations":[{"raw_affiliation_string":"Department of Information, Lijiang Culture And Tourism College, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5014632549"],"corresponding_institution_ids":["https://openalex.org/I204553293"],"apc_list":null,"apc_paid":null,"fwci":0.5493,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.67007358,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.8755999803543091,"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":0.8755999803543091,"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/T14225","display_name":"Advanced Sensor and Control Systems","score":0.8486999869346619,"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":0.8446000218391418,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5811656713485718}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5811656713485718}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3487075.3487134","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3487075.3487134","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 5th International Conference on Computer Science and Application Engineering","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/2","display_name":"Zero hunger","score":0.47999998927116394}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W2157883849","https://openalex.org/W2158485497","https://openalex.org/W2168020168","https://openalex.org/W2295598076","https://openalex.org/W2463813940","https://openalex.org/W2749469030","https://openalex.org/W2794987257","https://openalex.org/W2800341923","https://openalex.org/W2885055792","https://openalex.org/W2896291989","https://openalex.org/W2946304729","https://openalex.org/W3033830825","https://openalex.org/W3082233933","https://openalex.org/W3092047850","https://openalex.org/W3128690092","https://openalex.org/W4298255286"],"related_works":["https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W2382290278","https://openalex.org/W2478288626","https://openalex.org/W2350741829","https://openalex.org/W2530322880","https://openalex.org/W1596801655"],"abstract_inverted_index":{"Remaining":[0],"Useful":[1],"Life":[2],"(RUL)":[3],"prediction":[4,64],"is":[5,65,91,130],"an":[6],"essential":[7],"task":[8],"in":[9,132],"the":[10,35,48,59,124,137],"practice":[11],"of":[12,94,103,126],"predictive":[13],"maintenance":[14],"which":[15,42],"aims":[16],"at":[17],"repairing":[18],"equipment":[19],"before":[20],"it":[21,28],"fails":[22],"based":[23,128],"on":[24],"data":[25,45,85],"received":[26],"about":[27],"from":[29],"sensors.":[30],"Our":[31],"simulation":[32],"experiments":[33],"use":[34],"Turbofan":[36],"engine":[37],"degradation":[38],"dataset":[39,134],"CMAPSS":[40],"Data,":[41],"gained":[43],"historical":[44],"to":[46,57,115],"predict":[47],"remaining":[49],"useful":[50],"life":[51],"and":[52,82,87,109],"does":[53],"not":[54],"require":[55],"participants":[56],"consider":[58],"underlying":[60],"physical":[61],"factors.":[62],"RUL":[63],"performed":[66],"by":[67,106],"machine":[68,139],"learning":[69,96,140],"methods":[70],"including":[71],"Decision":[72],"Tree":[73],"(DT),":[74],"Random":[75],"Forest":[76],"(RF),":[77],"Support":[78],"Vector":[79],"Regression":[80],"(SVR),":[81],"XGBoost":[83,127],"after":[84],"pre-processing":[86],"feature":[88],"selection.":[89],"XGboost":[90],"a":[92,101,117],"kind":[93],"ensemble":[95],"algorithm":[97],"that":[98,123],"can":[99],"generate":[100],"series":[102],"weak":[104,113],"learners":[105,114],"continuous":[107],"training":[108],"then":[110],"combine":[111],"these":[112],"become":[116],"strong":[118],"learner.":[119],"Experimental":[120],"results":[121],"reveal":[122],"performance":[125],"model":[129],"effective":[131],"such":[133],"comparing":[135],"with":[136],"traditional":[138],"models.":[141]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
