{"id":"https://openalex.org/W2525715017","doi":"https://doi.org/10.1109/mmar.2016.7575259","title":"Reproduction of equipment wear characteristics with kernel regression","display_name":"Reproduction of equipment wear characteristics with kernel regression","publication_year":2016,"publication_date":"2016-08-01","ids":{"openalex":"https://openalex.org/W2525715017","doi":"https://doi.org/10.1109/mmar.2016.7575259","mag":"2525715017"},"language":"en","primary_location":{"id":"doi:10.1109/mmar.2016.7575259","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mmar.2016.7575259","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 21st International Conference on Methods and Models in Automation and Robotics (MMAR)","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/A5030518215","display_name":"Karol Koniuszewski","orcid":null},"institutions":[{"id":"https://openalex.org/I108403487","display_name":"Warsaw University of Technology","ror":"https://ror.org/00y0xnp53","country_code":"PL","type":"education","lineage":["https://openalex.org/I108403487"]}],"countries":["PL"],"is_corresponding":false,"raw_author_name":"Karol Koniuszewski","raw_affiliation_strings":["Politechnika Warszawska, Warszawa, PL"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Politechnika Warszawska, Warszawa, PL","institution_ids":["https://openalex.org/I108403487"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5035908112","display_name":"Pawe\u0142 D. Doma\u0144ski","orcid":"https://orcid.org/0000-0003-4053-3330"},"institutions":[{"id":"https://openalex.org/I108403487","display_name":"Warsaw University of Technology","ror":"https://ror.org/00y0xnp53","country_code":"PL","type":"education","lineage":["https://openalex.org/I108403487"]}],"countries":["PL"],"is_corresponding":false,"raw_author_name":"Pawel D. Domanski","raw_affiliation_strings":["Institute of Control and Computational Engineering, Warsaw University of Technology, Warsaw, Poland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Control and Computational Engineering, Warsaw University of Technology, Warsaw, Poland","institution_ids":["https://openalex.org/I108403487"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I108403487"],"apc_list":null,"apc_paid":null,"fwci":0.2674,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.62509246,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"168","issue":null,"first_page":"912","last_page":"917"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12169","display_name":"Non-Destructive Testing Techniques","score":0.9861999750137329,"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/T12169","display_name":"Non-Destructive Testing Techniques","score":0.9861999750137329,"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/T10876","display_name":"Fault Detection and Control Systems","score":0.9832000136375427,"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/T12282","display_name":"Mineral Processing and Grinding","score":0.9434999823570251,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/attrition","display_name":"Attrition","score":0.6705045700073242},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.4965119957923889},{"id":"https://openalex.org/keywords/scheduling","display_name":"Scheduling (production processes)","score":0.46095800399780273},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4438513517379761},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.42002588510513306},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.36861810088157654},{"id":"https://openalex.org/keywords/reliability-engineering","display_name":"Reliability engineering","score":0.32294750213623047},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3170011639595032},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.304953396320343},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.2976888418197632}],"concepts":[{"id":"https://openalex.org/C2780553607","wikidata":"https://www.wikidata.org/wiki/Q684868","display_name":"Attrition","level":2,"score":0.6705045700073242},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.4965119957923889},{"id":"https://openalex.org/C206729178","wikidata":"https://www.wikidata.org/wiki/Q2271896","display_name":"Scheduling (production processes)","level":2,"score":0.46095800399780273},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4438513517379761},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.42002588510513306},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.36861810088157654},{"id":"https://openalex.org/C200601418","wikidata":"https://www.wikidata.org/wiki/Q2193887","display_name":"Reliability engineering","level":1,"score":0.32294750213623047},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3170011639595032},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.304953396320343},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.2976888418197632},{"id":"https://openalex.org/C199343813","wikidata":"https://www.wikidata.org/wiki/Q12128","display_name":"Dentistry","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/mmar.2016.7575259","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mmar.2016.7575259","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 21st International Conference on Methods and Models in Automation and Robotics (MMAR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W40479804","https://openalex.org/W313907383","https://openalex.org/W564324191","https://openalex.org/W616910851","https://openalex.org/W1490488263","https://openalex.org/W1597926580","https://openalex.org/W1599057079","https://openalex.org/W1843747997","https://openalex.org/W1859781365","https://openalex.org/W1967969088","https://openalex.org/W2024866217","https://openalex.org/W2148922589","https://openalex.org/W2334018742","https://openalex.org/W2472373752","https://openalex.org/W2476521608","https://openalex.org/W2500090022","https://openalex.org/W2500834194","https://openalex.org/W2792296590","https://openalex.org/W2905148377","https://openalex.org/W2937114745","https://openalex.org/W4388290870","https://openalex.org/W6600569696","https://openalex.org/W6639100997"],"related_works":["https://openalex.org/W1969175355","https://openalex.org/W2007101045","https://openalex.org/W2056472298","https://openalex.org/W4389273703","https://openalex.org/W2586909830","https://openalex.org/W4231153204","https://openalex.org/W2775846538","https://openalex.org/W2116688168","https://openalex.org/W3124528783","https://openalex.org/W2900291040"],"abstract_inverted_index":{"Predictive":[0],"maintenance":[1],"task":[2],"is":[3,25,50,71,91],"of":[4,14,29],"crucial":[5],"role":[6],"for":[7],"any":[8,30],"plant":[9],"equipment":[10,103],"supervision":[11],"and":[12,43,77,85,95],"scheduling":[13],"service":[15],"activities.":[16],"For":[17],"this":[18],"purpose":[19],"it":[20],"should":[21],"be":[22],"known":[23],"what":[24],"current":[26,82],"aging":[27,55,63,87,104],"status":[28,84],"equipment.":[31],"Presented":[32],"approach":[33,90],"assumes":[34],"that":[35,53],"we":[36],"know":[37],"the":[38,54],"nominal":[39],"(starting)":[40],"element":[41,83],"curve":[42,75],"a":[44],"damage":[45],"one":[46],"as":[47],"well.":[48],"It":[49],"also":[51],"assumed":[52],"course":[56],"progresses":[57],"according":[58],"to":[59,73,80,101],"some":[60],"good":[61],"practice":[62],"Lorentz":[64],"attrition":[65],"(wear)":[66],"curve.":[67,88],"Kernel":[68],"Regression":[69],"algorithm":[70],"used":[72],"perform":[74],"adaptation":[76],"then":[78],"enabled":[79],"identify":[81],"varying":[86],"The":[89],"tested":[92],"on":[93],"SISO":[94],"two-dimensional":[96],"examples":[97],"proving":[98],"its":[99],"ability":[100],"reproduce":[102],"characteristics.":[105]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2017,"cited_by_count":1}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}