{"id":"https://openalex.org/W4292972564","doi":"https://doi.org/10.1109/tii.2022.3200428","title":"Noise-Aware Sparse Gaussian Processes and Application to Reliable Industrial Machinery Health Monitoring","display_name":"Noise-Aware Sparse Gaussian Processes and Application to Reliable Industrial Machinery Health Monitoring","publication_year":2022,"publication_date":"2022-08-22","ids":{"openalex":"https://openalex.org/W4292972564","doi":"https://doi.org/10.1109/tii.2022.3200428"},"language":"en","primary_location":{"id":"doi:10.1109/tii.2022.3200428","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tii.2022.3200428","pdf_url":null,"source":{"id":"https://openalex.org/S184777250","display_name":"IEEE Transactions on Industrial Informatics","issn_l":"1551-3203","issn":["1551-3203","1941-0050"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Industrial Informatics","raw_type":"journal-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/A5049301201","display_name":"Yang Jing-yu","orcid":"https://orcid.org/0000-0001-9751-075X"},"institutions":[{"id":"https://openalex.org/I47720641","display_name":"Huazhong University of Science and Technology","ror":"https://ror.org/00p991c53","country_code":"CN","type":"education","lineage":["https://openalex.org/I47720641"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jingyu Yang","raw_affiliation_strings":["Key Laboratory of Image Processing and Intelligent Control, Ministry of Education and School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China"],"raw_orcid":"https://orcid.org/0000-0001-9751-075X","affiliations":[{"raw_affiliation_string":"Key Laboratory of Image Processing and Intelligent Control, Ministry of Education and School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China","institution_ids":["https://openalex.org/I47720641"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048749917","display_name":"Zuogong Yue","orcid":"https://orcid.org/0000-0001-8457-2900"},"institutions":[{"id":"https://openalex.org/I47720641","display_name":"Huazhong University of Science and Technology","ror":"https://ror.org/00p991c53","country_code":"CN","type":"education","lineage":["https://openalex.org/I47720641"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zuogong Yue","raw_affiliation_strings":["Key Laboratory of Image Processing and Intelligent Control, Ministry of Education and School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China"],"raw_orcid":"https://orcid.org/0000-0001-8457-2900","affiliations":[{"raw_affiliation_string":"Key Laboratory of Image Processing and Intelligent Control, Ministry of Education and School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China","institution_ids":["https://openalex.org/I47720641"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5016488397","display_name":"Ye Yuan","orcid":"https://orcid.org/0000-0001-7858-0437"},"institutions":[{"id":"https://openalex.org/I47720641","display_name":"Huazhong University of Science and Technology","ror":"https://ror.org/00p991c53","country_code":"CN","type":"education","lineage":["https://openalex.org/I47720641"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ye Yuan","raw_affiliation_strings":["Key Laboratory of Image Processing and Intelligent Control, Ministry of Education and School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China"],"raw_orcid":"https://orcid.org/0000-0001-7858-0437","affiliations":[{"raw_affiliation_string":"Key Laboratory of Image Processing and Intelligent Control, Ministry of Education and School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China","institution_ids":["https://openalex.org/I47720641"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I47720641"],"apc_list":null,"apc_paid":null,"fwci":2.6977,"has_fulltext":false,"cited_by_count":28,"citation_normalized_percentile":{"value":0.90080333,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":"19","issue":"4","first_page":"5995","last_page":"6005"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10876","display_name":"Fault Detection and Control Systems","score":0.9965000152587891,"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/T10876","display_name":"Fault Detection and Control Systems","score":0.9965000152587891,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9902999997138977,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.9853000044822693,"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/interpretability","display_name":"Interpretability","score":0.8705110549926758},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6092276573181152},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5941449403762817},{"id":"https://openalex.org/keywords/condition-monitoring","display_name":"Condition monitoring","score":0.5641423463821411},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5627137422561646},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.5423096418380737},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5170310139656067},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5170177221298218},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.5099204778671265},{"id":"https://openalex.org/keywords/inference-engine","display_name":"Inference engine","score":0.4782737195491791},{"id":"https://openalex.org/keywords/predictive-maintenance","display_name":"Predictive maintenance","score":0.46490681171417236},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.45607924461364746},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.4473777711391449},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.4271199405193329},{"id":"https://openalex.org/keywords/reliability-engineering","display_name":"Reliability engineering","score":0.36926180124282837},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.3592737317085266},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.3288097381591797}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.8705110549926758},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6092276573181152},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5941449403762817},{"id":"https://openalex.org/C2775846686","wikidata":"https://www.wikidata.org/wiki/Q643012","display_name":"Condition monitoring","level":2,"score":0.5641423463821411},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5627137422561646},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.5423096418380737},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5170310139656067},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5170177221298218},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.5099204778671265},{"id":"https://openalex.org/C46743427","wikidata":"https://www.wikidata.org/wiki/Q1341685","display_name":"Inference engine","level":3,"score":0.4782737195491791},{"id":"https://openalex.org/C70452415","wikidata":"https://www.wikidata.org/wiki/Q3182448","display_name":"Predictive maintenance","level":2,"score":0.46490681171417236},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.45607924461364746},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4473777711391449},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.4271199405193329},{"id":"https://openalex.org/C200601418","wikidata":"https://www.wikidata.org/wiki/Q2193887","display_name":"Reliability engineering","level":1,"score":0.36926180124282837},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.3592737317085266},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.3288097381591797},{"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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.0},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tii.2022.3200428","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tii.2022.3200428","pdf_url":null,"source":{"id":"https://openalex.org/S184777250","display_name":"IEEE Transactions on Industrial Informatics","issn_l":"1551-3203","issn":["1551-3203","1941-0050"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Industrial Informatics","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.46000000834465027}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":50,"referenced_works":["https://openalex.org/W1571870753","https://openalex.org/W2032301033","https://openalex.org/W2048868027","https://openalex.org/W2104533781","https://openalex.org/W2105813220","https://openalex.org/W2119512810","https://openalex.org/W2125490399","https://openalex.org/W2146851580","https://openalex.org/W2187471809","https://openalex.org/W2194775991","https://openalex.org/W2754767460","https://openalex.org/W2789263499","https://openalex.org/W2790625295","https://openalex.org/W2808622270","https://openalex.org/W2809744928","https://openalex.org/W2815862320","https://openalex.org/W2940355724","https://openalex.org/W2946808246","https://openalex.org/W2953972663","https://openalex.org/W2962180662","https://openalex.org/W2964326308","https://openalex.org/W2977117446","https://openalex.org/W2989818023","https://openalex.org/W2995140071","https://openalex.org/W3000384844","https://openalex.org/W3000508506","https://openalex.org/W3015173390","https://openalex.org/W3015630033","https://openalex.org/W3015704045","https://openalex.org/W3017186475","https://openalex.org/W3032333158","https://openalex.org/W3041070623","https://openalex.org/W3044676633","https://openalex.org/W3086959365","https://openalex.org/W3106449828","https://openalex.org/W3122347867","https://openalex.org/W3138613066","https://openalex.org/W3160361541","https://openalex.org/W3163539034","https://openalex.org/W3170755416","https://openalex.org/W4206495689","https://openalex.org/W4210907964","https://openalex.org/W4225926312","https://openalex.org/W4226065182","https://openalex.org/W4237210482","https://openalex.org/W4389739628","https://openalex.org/W6605566567","https://openalex.org/W6675740164","https://openalex.org/W6676327744","https://openalex.org/W6736412012"],"related_works":["https://openalex.org/W2527510741","https://openalex.org/W2538175343","https://openalex.org/W2495537019","https://openalex.org/W3195564279","https://openalex.org/W57526933","https://openalex.org/W2337958200","https://openalex.org/W4361274616","https://openalex.org/W2340733335","https://openalex.org/W2908973203","https://openalex.org/W1660921355"],"abstract_inverted_index":{"Maintenance":[0],"of":[1,17,81,116],"machinery":[2],"equipment":[3],"in":[4],"smart":[5],"manufacturing":[6],"requires":[7],"real-time":[8],"health":[9,24],"monitoring,":[10],"strongly":[11],"supported":[12],"by":[13,29],"the":[14,74,104,117],"rapid":[15],"evolution":[16],"Artificial":[18],"Intelligence":[19],"(AI)":[20],"technologies.":[21],"Most":[22],"AI-based":[23],"monitoring":[25,36,39],"systems":[26,40],"are":[27,79],"powered":[28],"advanced":[30],"modeling":[31],"methods":[32],"and":[33,61,84,110,131],"intensive":[34],"high-quality":[35],"data.":[37],"Such":[38],"center":[41],"on":[42,92],"high-accuracy":[43],"predictive":[44],"performance":[45],"but":[46],"cannot":[47],"necessarily":[48],"convey":[49],"reliability,":[50],"such":[51],"as":[52],"satisfactory":[53],"resistance":[54],"to":[55,102],"strong":[56,89],"noises,":[57],"credible":[58,85],"uncertainty":[59,86],"analysis,":[60],"model":[62,101],"interpretability.":[63],"This":[64],"article":[65],"novelly":[66],"proposes":[67],"noise-aware":[68],"sparse":[69],"Gaussian":[70],"processes":[71],"(NASGP)":[72],"within":[73],"Bayesian":[75],"inference":[76,108],"framework.":[77],"NASGP":[78],"capable":[80],"consistent":[82],"high-performance":[83],"assessment":[87],"under":[88],"noises.":[90],"Based":[91],"NASGP,":[93],"we":[94],"then":[95],"develop":[96],"an":[97],"explainable":[98],"generalized":[99],"additive":[100],"bridge":[103],"gap":[105],"between":[106],"latent":[107],"mechanism":[109],"domain":[111],"expert":[112],"knowledge.":[113],"The":[114],"efficacy":[115],"proposed":[118],"approach":[119],"is":[120],"corroborated":[121],"through":[122],"two":[123],"case":[124],"studies":[125],"including":[126],"remaining":[127],"useful":[128],"life":[129],"prognosis":[130],"fault":[132],"diagnosis":[133],"for":[134],"rolling":[135],"bearings.":[136]},"counts_by_year":[{"year":2026,"cited_by_count":5},{"year":2025,"cited_by_count":10},{"year":2024,"cited_by_count":10},{"year":2023,"cited_by_count":3}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}