{"id":"https://openalex.org/W2020856737","doi":"https://doi.org/10.1145/2811411.2811509","title":"State transition of wind turbines based on empirical inference on model residuals","display_name":"State transition of wind turbines based on empirical inference on model residuals","publication_year":2015,"publication_date":"2015-10-09","ids":{"openalex":"https://openalex.org/W2020856737","doi":"https://doi.org/10.1145/2811411.2811509","mag":"2020856737"},"language":"en","primary_location":{"id":"doi:10.1145/2811411.2811509","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2811411.2811509","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2015 Conference on research in adaptive and convergent systems","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/A5053072850","display_name":"J\u00fcrgen Herp","orcid":"https://orcid.org/0000-0003-1799-8551"},"institutions":[{"id":"https://openalex.org/I177969490","display_name":"University of Southern Denmark","ror":"https://ror.org/03yrrjy16","country_code":"DK","type":"education","lineage":["https://openalex.org/I177969490"]}],"countries":["DK"],"is_corresponding":true,"raw_author_name":"J\u00fcrgen Herp","raw_affiliation_strings":["University of Southern Denmark, Odense, Denmark","University of Southern Denmark, Odense (Denmark)"],"affiliations":[{"raw_affiliation_string":"University of Southern Denmark, Odense, Denmark","institution_ids":["https://openalex.org/I177969490"]},{"raw_affiliation_string":"University of Southern Denmark, Odense (Denmark)","institution_ids":["https://openalex.org/I177969490"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5070484360","display_name":"Hossein Ramezani","orcid":"https://orcid.org/0000-0002-2273-1520"},"institutions":[{"id":"https://openalex.org/I177969490","display_name":"University of Southern Denmark","ror":"https://ror.org/03yrrjy16","country_code":"DK","type":"education","lineage":["https://openalex.org/I177969490"]}],"countries":["DK"],"is_corresponding":false,"raw_author_name":"Mohammad H. Ramezani","raw_affiliation_strings":["University of Southern Denmark, Odense, Denmark","University of Southern Denmark, Odense (Denmark)"],"affiliations":[{"raw_affiliation_string":"University of Southern Denmark, Odense, Denmark","institution_ids":["https://openalex.org/I177969490"]},{"raw_affiliation_string":"University of Southern Denmark, Odense (Denmark)","institution_ids":["https://openalex.org/I177969490"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5053072850"],"corresponding_institution_ids":["https://openalex.org/I177969490"],"apc_list":null,"apc_paid":null,"fwci":0.6003,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.72439521,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"32","last_page":"37"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.984000027179718,"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"}},"topics":[{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.984000027179718,"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"}},{"id":"https://openalex.org/T13891","display_name":"Engineering Diagnostics and Reliability","score":0.9484000205993652,"subfield":{"id":"https://openalex.org/subfields/2211","display_name":"Mechanics of Materials"},"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":0.9318000078201294,"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/wind-power","display_name":"Wind power","score":0.6384122371673584},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6258577704429626},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5013675689697266},{"id":"https://openalex.org/keywords/state","display_name":"State (computer science)","score":0.4977097809314728},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.41286253929138184},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2180703580379486},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.20911157131195068},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.20364797115325928},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.20064625144004822}],"concepts":[{"id":"https://openalex.org/C78600449","wikidata":"https://www.wikidata.org/wiki/Q43302","display_name":"Wind power","level":2,"score":0.6384122371673584},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6258577704429626},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5013675689697266},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.4977097809314728},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.41286253929138184},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2180703580379486},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.20911157131195068},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.20364797115325928},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.20064625144004822},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/2811411.2811509","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2811411.2811509","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2015 Conference on research in adaptive and convergent systems","raw_type":"proceedings-article"},{"id":"pmh:oai:sdu.dk:openaire_cris_publications/94b5ae11-e3e4-49fe-bbea-5008dd2fb267","is_oa":false,"landing_page_url":"https://portal.findresearcher.sdu.dk/da/publications/94b5ae11-e3e4-49fe-bbea-5008dd2fb267","pdf_url":null,"source":{"id":"https://openalex.org/S4306400423","display_name":"University of Southern Denmark Research Portal (University of Southern Denmark)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I177969490","host_organization_name":"University of Southern Denmark","host_organization_lineage":["https://openalex.org/I177969490"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Herp, J & Ramezani, M H 2015, State Transition of Wind Turbines based on Empirical Inference on Model Residuals. in Proceedings of the 2015 Conference on research in adaptive and convergent systems. Association for Computing Machinery, pp. 32-37, Conference on research in adaptive and convergent systems, Prague, Czech Republic, 09/10/2015. https://doi.org/10.1145/2811411.2811509","raw_type":"info:eu-repo/semantics/publishedVersion"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","score":0.8899999856948853,"display_name":"Affordable and clean energy"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W31077237","https://openalex.org/W169221066","https://openalex.org/W1483365869","https://openalex.org/W1494853941","https://openalex.org/W1554663460","https://openalex.org/W1965664474","https://openalex.org/W1998469123","https://openalex.org/W2025352971","https://openalex.org/W2043078029","https://openalex.org/W2048695734","https://openalex.org/W2081842931","https://openalex.org/W2084840427","https://openalex.org/W2094414211","https://openalex.org/W2130663204","https://openalex.org/W2131065845","https://openalex.org/W2159577749","https://openalex.org/W2185662903","https://openalex.org/W2225533476","https://openalex.org/W2624822927","https://openalex.org/W2964132830","https://openalex.org/W4248681815","https://openalex.org/W4255222813","https://openalex.org/W4285719527","https://openalex.org/W4408741816","https://openalex.org/W6656588140"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052"],"abstract_inverted_index":{"In":[0],"this":[1],"study,":[2],"we":[3,195],"propose":[4,86],"a":[5,87,135,149,207,214],"method":[6],"to":[7,46,90,115,147,170,219],"monitor":[8],"state":[9,42,91,108,157,183],"transitions":[10],"for":[11,98,188],"wind":[12,26],"turbines":[13],"based":[14,93,133],"on":[15,19,34,94,134,174,182,228],"the":[16,48,59,75,80,99,103,106,139,152,155,163,172,175,198,201,224,229],"online":[17,100,119],"inference":[18,120,173,181],"model":[20,132],"residuals.":[21],"Slowly":[22],"developing":[23],"faults":[24],"in":[25,58,168],"turbine":[27],"can,":[28],"when":[29],"not":[30],"detected":[31],"and":[32,39,53,61,191,194,211],"fixed":[33,190,218],"time,":[35],"cause":[36],"severe":[37],"damage":[38,52],"downtime.":[40],"Early":[41],"transition":[43,92,150,184],"detection":[44,68],"attempts":[45],"reduce":[47],"risk":[49],"of":[50,82,112,200],"sever":[51],"downtime":[54],"by":[55],"recognizing":[56],"changes":[57],"data":[60,153,215],"adapted":[62],"predictive":[63,131,230],"models":[64],"appropriately.":[65],"As":[66],"fault":[67],"studies":[69],"often":[70],"deal":[71],"with":[72,79],"hard":[73],"thresholds,":[74],"Bayesian":[76,88,180],"analysis":[77],"comes":[78],"advantage":[81],"probability":[83],"measures.":[84],"We":[85,177],"approach":[89],"hidden":[95],"variables":[96],"relevant":[97],"predictor,":[101],"namely":[102],"time":[104,126],"since":[105,154],"last":[107,156],"transition.":[109,158],"It":[110,159],"is":[111,146,160],"great":[113],"interest":[114],"see":[116],"that":[117,162,179,197],"exact":[118],"can":[121,185,203],"be":[122,186,204],"performed":[123,187],"at":[124],"every":[125],"step,":[127],"given":[128,151,213],"an":[129],"underlying":[130],"hazard":[136,140],"function.":[137],"Here":[138],"function":[141],"describes":[142],"how":[143],"likely":[144],"it":[145],"undergo":[148],"imperative":[161],"hyper-parameters":[164,202,221],"are":[165],"known":[166,192],"before-hand":[167],"order":[169],"perform":[171],"model.":[176],"show":[178],"assumed":[189],"hyper-parameters,":[193],"emphasize":[196],"selection":[199],"treated":[205],"as":[206],"machine":[208],"learning":[209],"problem":[210],"trained":[212],"set.":[216],"Comparing":[217],"learned":[220],"points":[222],"out":[223],"impact":[225],"they":[226],"have":[227],"performance.":[231]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":2}],"updated_date":"2026-03-10T16:38:18.471706","created_date":"2025-10-10T00:00:00"}