{"id":"https://openalex.org/W4390043275","doi":"https://doi.org/10.1145/3627915.3628078","title":"Predicting Remaining Service Life of Concrete Pump Pistons Using Ensemble Learning","display_name":"Predicting Remaining Service Life of Concrete Pump Pistons Using Ensemble Learning","publication_year":2023,"publication_date":"2023-10-17","ids":{"openalex":"https://openalex.org/W4390043275","doi":"https://doi.org/10.1145/3627915.3628078"},"language":"en","primary_location":{"id":"doi:10.1145/3627915.3628078","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3627915.3628078","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 7th International Conference on Computer Science and Application Engineering","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/A5033156009","display_name":"Xinxin Xie","orcid":"https://orcid.org/0009-0003-0513-370X"},"institutions":[{"id":"https://openalex.org/I4800084","display_name":"Southwest Jiaotong University","ror":"https://ror.org/00hn7w693","country_code":"CN","type":"education","lineage":["https://openalex.org/I4800084"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinxin Xie","raw_affiliation_strings":["School of Mechanical Engineering, Southwest Jiaotong University, China"],"raw_orcid":"https://orcid.org/0009-0003-0513-370X","affiliations":[{"raw_affiliation_string":"School of Mechanical Engineering, Southwest Jiaotong University, China","institution_ids":["https://openalex.org/I4800084"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Xiangyin Meng","orcid":"https://orcid.org/0000-0003-4716-9324"},"institutions":[{"id":"https://openalex.org/I4800084","display_name":"Southwest Jiaotong University","ror":"https://ror.org/00hn7w693","country_code":"CN","type":"education","lineage":["https://openalex.org/I4800084"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiangyin Meng","raw_affiliation_strings":["School of Mechanical Engineering, Southwest Jiaotong University, China"],"raw_orcid":"https://orcid.org/0000-0003-4716-9324","affiliations":[{"raw_affiliation_string":"School of Mechanical Engineering, Southwest Jiaotong University, China","institution_ids":["https://openalex.org/I4800084"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086277024","display_name":"Jinlan Du","orcid":"https://orcid.org/0009-0008-3886-1398"},"institutions":[{"id":"https://openalex.org/I4800084","display_name":"Southwest Jiaotong University","ror":"https://ror.org/00hn7w693","country_code":"CN","type":"education","lineage":["https://openalex.org/I4800084"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinlan Du","raw_affiliation_strings":["School of Mechanical Engineering, Southwest Jiaotong University, China"],"raw_orcid":"https://orcid.org/0009-0008-3886-1398","affiliations":[{"raw_affiliation_string":"School of Mechanical Engineering, Southwest Jiaotong University, China","institution_ids":["https://openalex.org/I4800084"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5000693521","display_name":"Zhicheng Peng","orcid":"https://orcid.org/0009-0002-6708-3069"},"institutions":[{"id":"https://openalex.org/I4800084","display_name":"Southwest Jiaotong University","ror":"https://ror.org/00hn7w693","country_code":"CN","type":"education","lineage":["https://openalex.org/I4800084"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhicheng Peng","raw_affiliation_strings":["School of Mechanical Engineering, Southwest Jiaotong University, China"],"raw_orcid":"https://orcid.org/0009-0002-6708-3069","affiliations":[{"raw_affiliation_string":"School of Mechanical Engineering, Southwest Jiaotong University, China","institution_ids":["https://openalex.org/I4800084"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4800084"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"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/T11372","display_name":"Hydraulic and Pneumatic Systems","score":0.992900013923645,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical 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/T11372","display_name":"Hydraulic and Pneumatic Systems","score":0.992900013923645,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical 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/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.982200026512146,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural 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":0.9790999889373779,"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/downtime","display_name":"Downtime","score":0.8710491061210632},{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.8180549144744873},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.6802217960357666},{"id":"https://openalex.org/keywords/unavailability","display_name":"Unavailability","score":0.6775031685829163},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6771605014801025},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.5952800512313843},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5885245203971863},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4819581210613251},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4420575499534607},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.42929667234420776},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.4161495566368103},{"id":"https://openalex.org/keywords/linear-regression","display_name":"Linear regression","score":0.4143916964530945},{"id":"https://openalex.org/keywords/reliability-engineering","display_name":"Reliability engineering","score":0.34540194272994995},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.18453145027160645},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.14307868480682373}],"concepts":[{"id":"https://openalex.org/C180591934","wikidata":"https://www.wikidata.org/wiki/Q1253369","display_name":"Downtime","level":2,"score":0.8710491061210632},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.8180549144744873},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.6802217960357666},{"id":"https://openalex.org/C2780505938","wikidata":"https://www.wikidata.org/wiki/Q17093282","display_name":"Unavailability","level":2,"score":0.6775031685829163},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6771605014801025},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.5952800512313843},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5885245203971863},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4819581210613251},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4420575499534607},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.42929667234420776},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.4161495566368103},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.4143916964530945},{"id":"https://openalex.org/C200601418","wikidata":"https://www.wikidata.org/wiki/Q2193887","display_name":"Reliability engineering","level":1,"score":0.34540194272994995},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.18453145027160645},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.14307868480682373},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3627915.3628078","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3627915.3628078","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 7th International Conference on Computer Science and Application Engineering","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W2208652546","https://openalex.org/W2295598076","https://openalex.org/W2770203925","https://openalex.org/W2950150695","https://openalex.org/W2977718750","https://openalex.org/W3040694753","https://openalex.org/W3200240018","https://openalex.org/W3205017803","https://openalex.org/W3215112623","https://openalex.org/W6601131277","https://openalex.org/W6821777662"],"related_works":["https://openalex.org/W4237235066","https://openalex.org/W207884067","https://openalex.org/W3127016596","https://openalex.org/W2026539069","https://openalex.org/W2519114609","https://openalex.org/W2891676548","https://openalex.org/W2479186422","https://openalex.org/W3184570916","https://openalex.org/W1706194612","https://openalex.org/W1038900426"],"abstract_inverted_index":{"To":[0],"address":[1],"the":[2,14,40,50,83,91,102,106,110,114,123,130],"issue":[3],"of":[4,52,86,105,126,132],"unplanned":[5],"downtime":[6,20],"caused":[7],"by":[8],"concrete":[9,127],"pump":[10],"piston":[11],"failures":[12],"and":[13,18,46,71,81,109,129],"high":[15],"maintenance":[16],"costs":[17],"extended":[19],"resulting":[21],"from":[22],"preventative":[23],"maintenance,":[24],"a":[25,53],"multi-model":[26],"fusion":[27,111],"residual":[28,77],"life":[29,78],"prediction":[30,55,79,84,97,103],"method":[31],"based":[32],"on":[33,113],"Stacking":[34,92],"ensemble":[35,93],"learning":[36,94],"is":[37,43,99],"proposed.":[38],"As":[39],"sample":[41],"data":[42],"small-batch,":[44],"high-dimensional,":[45],"non-linear":[47,66],"heterogeneous":[48],"data,":[49],"results":[51],"single":[54,107],"model":[56,108,112],"are":[57,117],"prone":[58],"to":[59,75],"overfitting.":[60],"Therefore,":[61],"this":[62],"study":[63],"uses":[64],"three":[65],"regression":[67],"algorithms,":[68],"SVR,":[69],"XGBoost,":[70],"BP":[72],"neural":[73],"network,":[74],"establish":[76],"models,":[80],"compares":[82],"accuracy":[85,98,104],"each":[87],"model.":[88],"By":[89],"using":[90],"method,":[95],"higher":[96],"achieved.":[100],"Finally,":[101],"test":[115],"samples":[116],"compared,":[118],"which":[119],"provides":[120],"guidance":[121],"for":[122],"predictive":[124],"replacement":[125],"pistons":[128],"diagnosis":[131],"mechanical":[133],"component":[134],"failures.":[135]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}