{"id":"https://openalex.org/W3205871783","doi":"https://doi.org/10.1109/indin45523.2021.9557422","title":"Anomaly Detection in the Time Series Data from Fehn Pollux Ship with ECO Flettner Rotor","display_name":"Anomaly Detection in the Time Series Data from Fehn Pollux Ship with ECO Flettner Rotor","publication_year":2021,"publication_date":"2021-07-21","ids":{"openalex":"https://openalex.org/W3205871783","doi":"https://doi.org/10.1109/indin45523.2021.9557422","mag":"3205871783"},"language":"en","primary_location":{"id":"doi:10.1109/indin45523.2021.9557422","is_oa":false,"landing_page_url":"https://doi.org/10.1109/indin45523.2021.9557422","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE 19th International Conference on Industrial Informatics (INDIN)","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/A5085973852","display_name":"Farzaneh Nourmohammadi","orcid":"https://orcid.org/0009-0003-5121-835X"},"institutions":[{"id":"https://openalex.org/I4210104665","display_name":"University of Applied Sciences Emden Leer","ror":"https://ror.org/01bc76c69","country_code":"DE","type":"education","lineage":["https://openalex.org/I4210104665"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Farzaneh Nourmohammadi","raw_affiliation_strings":["University of Applied Sciences Emden/Leer,Faculty of Technology,Emden,Germany"],"affiliations":[{"raw_affiliation_string":"University of Applied Sciences Emden/Leer,Faculty of Technology,Emden,Germany","institution_ids":["https://openalex.org/I4210104665"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049034545","display_name":"Allanazar Jumabayev","orcid":null},"institutions":[{"id":"https://openalex.org/I4210104665","display_name":"University of Applied Sciences Emden Leer","ror":"https://ror.org/01bc76c69","country_code":"DE","type":"education","lineage":["https://openalex.org/I4210104665"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Allanazar Jumabayev","raw_affiliation_strings":["University of Applied Sciences Emden/Leer,Faculty of Technology,Emden,Germany"],"affiliations":[{"raw_affiliation_string":"University of Applied Sciences Emden/Leer,Faculty of Technology,Emden,Germany","institution_ids":["https://openalex.org/I4210104665"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5012415874","display_name":"Elmar Wings","orcid":"https://orcid.org/0000-0001-9532-5163"},"institutions":[{"id":"https://openalex.org/I4210104665","display_name":"University of Applied Sciences Emden Leer","ror":"https://ror.org/01bc76c69","country_code":"DE","type":"education","lineage":["https://openalex.org/I4210104665"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Elmar Wings","raw_affiliation_strings":["University of Applied Sciences Emden/Leer,Faculty of Technology,Emden,Germany"],"affiliations":[{"raw_affiliation_string":"University of Applied Sciences Emden/Leer,Faculty of Technology,Emden,Germany","institution_ids":["https://openalex.org/I4210104665"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5085973852"],"corresponding_institution_ids":["https://openalex.org/I4210104665"],"apc_list":null,"apc_paid":null,"fwci":0.2719,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.63864031,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9839000105857849,"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"}},"topics":[{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9839000105857849,"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/T10876","display_name":"Fault Detection and Control Systems","score":0.944599986076355,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9404000043869019,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.6797922253608704},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.6239975094795227},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5760096311569214},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.4949163794517517},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.46560317277908325},{"id":"https://openalex.org/keywords/rotor","display_name":"Rotor (electric)","score":0.4309893250465393},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3214600086212158},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.15257498621940613},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.14913836121559143},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.09767431020736694},{"id":"https://openalex.org/keywords/electrical-engineering","display_name":"Electrical engineering","score":0.06766358017921448},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.04943573474884033}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.6797922253608704},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.6239975094795227},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5760096311569214},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.4949163794517517},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.46560317277908325},{"id":"https://openalex.org/C17281054","wikidata":"https://www.wikidata.org/wiki/Q193466","display_name":"Rotor (electric)","level":2,"score":0.4309893250465393},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3214600086212158},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.15257498621940613},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.14913836121559143},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.09767431020736694},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.06766358017921448},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.04943573474884033},{"id":"https://openalex.org/C26873012","wikidata":"https://www.wikidata.org/wiki/Q214781","display_name":"Condensed matter physics","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/indin45523.2021.9557422","is_oa":false,"landing_page_url":"https://doi.org/10.1109/indin45523.2021.9557422","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE 19th International Conference on Industrial Informatics (INDIN)","raw_type":"proceedings-article"},{"id":"pmh:oai:opus.hs-emden-leer.de:830","is_oa":false,"landing_page_url":"https://opus.hs-emden-leer.de/frontdoor/index/index/docId/830","pdf_url":null,"source":{"id":"https://openalex.org/S7407053446","display_name":"Hochschulschriftenserver der Hochschule Emden/Leer","issn_l":null,"issn":[],"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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"doc-type:conferenceObject"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Affordable and clean energy","score":0.8999999761581421,"id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W1876967670","https://openalex.org/W2033626294","https://openalex.org/W2034841614","https://openalex.org/W2049058890","https://openalex.org/W2137690751","https://openalex.org/W2161200291","https://openalex.org/W2600438920","https://openalex.org/W2766183090","https://openalex.org/W2788416735","https://openalex.org/W2947330499","https://openalex.org/W2963914175","https://openalex.org/W2977545921","https://openalex.org/W3003386219","https://openalex.org/W3107249503","https://openalex.org/W3170851865","https://openalex.org/W4234967753","https://openalex.org/W6737020183"],"related_works":["https://openalex.org/W2806741695","https://openalex.org/W4290647774","https://openalex.org/W3189286258","https://openalex.org/W3207797160","https://openalex.org/W3210364259","https://openalex.org/W4300558037","https://openalex.org/W2667207928","https://openalex.org/W2912112202","https://openalex.org/W4377864969","https://openalex.org/W3120251014"],"abstract_inverted_index":{"An":[0],"ECO":[1],"Flettner":[2],"rotor":[3,40,97],"has":[4],"been":[5],"installed":[6],"on":[7],"board":[8],"the":[9,16,43,51,67,73,80,83,108],"vessel":[10],"MV":[11],"Fehn":[12],"Pollux":[13],"to":[14,65],"reduce":[15],"vessel\u2019s":[17,44],"carbon":[18],"emissions":[19],"and":[20,39,47,78,96,113],"save":[21],"fuel.":[22],"The":[23,105],"extent":[24],"of":[25,32,75,82],"fuel-saving":[26],"is":[27],"assessed":[28],"using":[29],"recorded":[30],"data":[31,45,52],"apparent":[33,36,90,93],"wind":[34,37,91,94],"speed,":[35,92,98],"angle,":[38,95],"speed":[41],"by":[42,56],"acquisition":[46],"storage":[48],"system.":[49],"However,":[50],"contains":[53],"anomalies":[54,62,88],"caused":[55],"noise,":[57],"vibration,":[58],"or":[59],"errors.":[60],"Detecting":[61],"could":[63],"help":[64],"understand":[66],"reason":[68],"for":[69],"their":[70,119],"occurrence,":[71],"improve":[72],"calculation":[74],"energy":[76],"savings,":[77],"increase":[79],"accuracy":[81],"trained":[84],"models.":[85],"To":[86],"detect":[87],"in":[89],"three":[99],"anomaly":[100,110,126],"detection":[101,111,127],"approaches":[102],"are":[103],"proposed.":[104],"paper":[106],"describes":[107],"proposed":[109,125],"concepts,":[112],"it":[114,123],"gives":[115],"an":[116],"insight":[117],"into":[118],"implementation":[120],"process.":[121],"Additionally,":[122],"evaluates":[124],"capabilities.":[128]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
