{"id":"https://openalex.org/W4385986763","doi":"https://doi.org/10.3390/s23167239","title":"An Unsupervised Machine Learning Approach for Monitoring Data Fusion and Health Indicator Construction","display_name":"An Unsupervised Machine Learning Approach for Monitoring Data Fusion and Health Indicator Construction","publication_year":2023,"publication_date":"2023-08-18","ids":{"openalex":"https://openalex.org/W4385986763","doi":"https://doi.org/10.3390/s23167239","pmid":"https://pubmed.ncbi.nlm.nih.gov/37631775"},"language":"en","primary_location":{"id":"doi:10.3390/s23167239","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s23167239","pdf_url":"https://www.mdpi.com/1424-8220/23/16/7239/pdf?version=1692322133","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/1424-8220/23/16/7239/pdf?version=1692322133","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5018504870","display_name":"Lin Huang","orcid":"https://orcid.org/0000-0002-6568-8331"},"institutions":[{"id":"https://openalex.org/I2800710378","display_name":"Naval University of Engineering","ror":"https://ror.org/056vyez31","country_code":"CN","type":"education","lineage":["https://openalex.org/I2800710378"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lin Huang","raw_affiliation_strings":["Ship Comprehensive Test and Training Base, Naval University of Engineering, Wuhan 430033, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ship Comprehensive Test and Training Base, Naval University of Engineering, Wuhan 430033, China","institution_ids":["https://openalex.org/I2800710378"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100606628","display_name":"Xin Pan","orcid":"https://orcid.org/0000-0001-5326-4323"},"institutions":[{"id":"https://openalex.org/I2800710378","display_name":"Naval University of Engineering","ror":"https://ror.org/056vyez31","country_code":"CN","type":"education","lineage":["https://openalex.org/I2800710378"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xin Pan","raw_affiliation_strings":["School of Electrical Engineering, Naval University of Engineering, Wuhan 430033, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical Engineering, Naval University of Engineering, Wuhan 430033, China","institution_ids":["https://openalex.org/I2800710378"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112084627","display_name":"Yajie Liu","orcid":"https://orcid.org/0009-0007-9575-5834"},"institutions":[{"id":"https://openalex.org/I2800710378","display_name":"Naval University of Engineering","ror":"https://ror.org/056vyez31","country_code":"CN","type":"education","lineage":["https://openalex.org/I2800710378"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yajie Liu","raw_affiliation_strings":["Ship Comprehensive Test and Training Base, Naval University of Engineering, Wuhan 430033, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ship Comprehensive Test and Training Base, Naval University of Engineering, Wuhan 430033, China","institution_ids":["https://openalex.org/I2800710378"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100864830","display_name":"Li Gong","orcid":null},"institutions":[{"id":"https://openalex.org/I2800710378","display_name":"Naval University of Engineering","ror":"https://ror.org/056vyez31","country_code":"CN","type":"education","lineage":["https://openalex.org/I2800710378"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Li Gong","raw_affiliation_strings":["Ship Comprehensive Test and Training Base, Naval University of Engineering, Wuhan 430033, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ship Comprehensive Test and Training Base, Naval University of Engineering, Wuhan 430033, China","institution_ids":["https://openalex.org/I2800710378"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5100864830"],"corresponding_institution_ids":["https://openalex.org/I2800710378"],"apc_list":{"value":2400,"currency":"CHF","value_usd":2673},"apc_paid":{"value":2400,"currency":"CHF","value_usd":2673},"fwci":1.3912,"has_fulltext":true,"cited_by_count":12,"citation_normalized_percentile":{"value":0.79393161,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"23","issue":"16","first_page":"7239","last_page":"7239"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.996999979019165,"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":0.996999979019165,"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/T10780","display_name":"Reliability and Maintenance Optimization","score":0.9948999881744385,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"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":0.98580002784729,"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/autoencoder","display_name":"Autoencoder","score":0.7891832590103149},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7197182774543762},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6422760486602783},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6096067428588867},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6062314510345459},{"id":"https://openalex.org/keywords/predictability","display_name":"Predictability","score":0.5873385667800903},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5721985101699829},{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"Principal component analysis","score":0.4934333860874176},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4195255637168884}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.7891832590103149},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7197182774543762},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6422760486602783},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6096067428588867},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6062314510345459},{"id":"https://openalex.org/C197640229","wikidata":"https://www.wikidata.org/wiki/Q2534066","display_name":"Predictability","level":2,"score":0.5873385667800903},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5721985101699829},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.4934333860874176},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4195255637168884},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":5,"locations":[{"id":"doi:10.3390/s23167239","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s23167239","pdf_url":"https://www.mdpi.com/1424-8220/23/16/7239/pdf?version=1692322133","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},{"id":"pmid:37631775","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/37631775","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors (Basel, Switzerland)","raw_type":null},{"id":"pmh:oai:pubmedcentral.nih.gov:10459474","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/10459474","pdf_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC10459474/pdf/sensors-23-07239.pdf","source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sensors (Basel)","raw_type":"Text"},{"id":"pmh:oai:doaj.org/article:1bd61a0f4bc546ab925867f4008802be","is_oa":true,"landing_page_url":"https://doaj.org/article/1bd61a0f4bc546ab925867f4008802be","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":"Sensors, Vol 23, Iss 16, p 7239 (2023)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/1424-8220/23/16/7239/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/s23167239","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sensors; Volume 23; Issue 16; Pages: 7239","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/s23167239","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s23167239","pdf_url":"https://www.mdpi.com/1424-8220/23/16/7239/pdf?version=1692322133","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.5299999713897705,"display_name":"Responsible consumption and production","id":"https://metadata.un.org/sdg/12"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4385986763.pdf"},"referenced_works_count":44,"referenced_works":["https://openalex.org/W1540327028","https://openalex.org/W2025768430","https://openalex.org/W2092939357","https://openalex.org/W2120841219","https://openalex.org/W2154053567","https://openalex.org/W2522585561","https://openalex.org/W2617137613","https://openalex.org/W2922520136","https://openalex.org/W2952516470","https://openalex.org/W2955612236","https://openalex.org/W2991579784","https://openalex.org/W3002064451","https://openalex.org/W3011973438","https://openalex.org/W3020467718","https://openalex.org/W3020712220","https://openalex.org/W3033917083","https://openalex.org/W3044005235","https://openalex.org/W3087439412","https://openalex.org/W3094110601","https://openalex.org/W3111082827","https://openalex.org/W3113180780","https://openalex.org/W3141188857","https://openalex.org/W3143638720","https://openalex.org/W3146674346","https://openalex.org/W3158937816","https://openalex.org/W3161917984","https://openalex.org/W3173924324","https://openalex.org/W3193451403","https://openalex.org/W3200063094","https://openalex.org/W3206511446","https://openalex.org/W3209243216","https://openalex.org/W3212733794","https://openalex.org/W3214066484","https://openalex.org/W3216119358","https://openalex.org/W4200021389","https://openalex.org/W4200209943","https://openalex.org/W4205525990","https://openalex.org/W4213228517","https://openalex.org/W4214774301","https://openalex.org/W4224839115","https://openalex.org/W4283768516","https://openalex.org/W4308422219","https://openalex.org/W4323545743","https://openalex.org/W6765213864"],"related_works":["https://openalex.org/W2726467123","https://openalex.org/W2064726690","https://openalex.org/W4254065731","https://openalex.org/W4252678288","https://openalex.org/W1607297154","https://openalex.org/W4210820789","https://openalex.org/W2913177154","https://openalex.org/W4310873165","https://openalex.org/W2669956259","https://openalex.org/W4249005693"],"abstract_inverted_index":{"The":[0,116],"prediction":[1,33],"of":[2,18,34,91,134,144,162,189,207,232,245,249,268,293,302],"system":[3,35,261],"degradation":[4,36],"is":[5,80,104,119,138,297],"very":[6],"important":[7,13],"as":[8,11,37,140,166],"it":[9,38],"serves":[10],"an":[12,70,95,112],"basis":[14],"for":[15,42,60,101,154,169,178,284,299],"the":[16,32,87,124,131,135,141,145,160,187,190,198,220,224,230,243,246,250,260,277,281,290,300,308],"formulation":[17],"condition-based":[19,312],"maintenance":[20,310],"strategies.":[21],"An":[22],"effective":[23],"health":[24,65,92,142,291],"indicator":[25,66,143],"(HI)":[26],"plays":[27],"a":[28,58,74,108,213,294],"key":[29],"role":[30],"in":[31,82,183,229,239],"enables":[39],"vital":[40],"information":[41,168],"critical":[43],"tasks":[44],"ranging":[45],"from":[46],"fault":[47],"diagnosis":[48],"to":[49,85,158,185,241],"remaining":[50],"useful":[51],"life":[52],"prediction.":[53],"To":[54],"address":[55],"this":[56,83],"issue,":[57],"method":[59,97,283],"monitoring":[61,127,155],"data":[62,128,156,163],"fusion":[63],"and":[64,73,89,98,111,129,172,201,212,226,236,258,273,306],"construction":[67,103,132,286],"based":[68,106,122,196],"on":[69,107,123,197,253],"autoencoder":[71,110,215],"(AE)":[72],"long":[75],"short-term":[76],"memory":[77],"(LSTM)":[78],"network":[79,118],"proposed":[81,191,282],"study":[84],"improve":[86],"predictability":[88],"effectiveness":[90],"indicators.":[93],"Firstly,":[94],"unsupervised":[96],"overall":[99],"framework":[100],"HI":[102,254,262,285],"built":[105],"deep":[109],"LSTM":[113],"neural":[114,117],"network.":[115],"trained":[120],"fully":[121],"normal":[125],"operating":[126,179],"then":[130],"error":[133],"AE":[136],"model":[137,222,228,252],"adopted":[139],"system.":[146],"Secondly,":[147],"we":[148,256],"propose":[149],"related":[150],"machine":[151],"learning":[152],"techniques":[153],"processing":[157],"overcome":[159],"issue":[161],"fusion,":[164],"such":[165],"mutual":[167],"sensor":[170],"selection":[171],"t-distributed":[173],"stochastic":[174],"neighbor":[175],"embedding":[176],"(T-SNE)":[177],"condition":[180],"identification.":[181],"Thirdly,":[182],"order":[184,240],"verify":[186],"performance":[188],"method,":[192],"experiments":[193],"are":[194,203],"conducted":[195],"CMAPSS":[199],"dataset":[200],"results":[202,278],"compared":[204],"with":[205],"algorithms":[206],"principal":[208],"component":[209],"analysis":[210],"(PCA)":[211],"vanilla":[214],"model.":[216],"Result":[217],"shows":[218],"that":[219,280],"LSTM-AE":[221],"outperforms":[223],"PCA":[225],"Vanilla-AE":[227],"metrics":[231],"monotonicity,":[233],"trendability,":[234],"prognosability,":[235],"fitness.":[237],"Fourthly,":[238],"analyze":[242,259],"impact":[244],"time":[247,266],"step":[248],"LSMT-AE":[251],"construction,":[255],"construct":[257],"curve":[263],"under":[264],"different":[265],"steps":[267],"5,":[269],"10,":[270],"15,":[271],"20,":[272],"25":[274],"cycles.":[275],"Finally,":[276],"demonstrate":[279],"can":[287],"effectively":[288],"characterize":[289],"state":[292],"system,":[295],"which":[296],"helpful":[298],"development":[301],"further":[303],"failure":[304],"prognostics":[305],"converting":[307],"scheduled":[309],"into":[311],"maintenance.":[313]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":7},{"year":2024,"cited_by_count":2}],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2025-10-10T00:00:00"}
