{"id":"https://openalex.org/W3039852820","doi":"https://doi.org/10.1109/percom45495.2020.9127357","title":"Predicting Machine Errors based on Adaptive Sensor Data Drifts in a Real World Industrial Setup","display_name":"Predicting Machine Errors based on Adaptive Sensor Data Drifts in a Real World Industrial Setup","publication_year":2020,"publication_date":"2020-03-01","ids":{"openalex":"https://openalex.org/W3039852820","doi":"https://doi.org/10.1109/percom45495.2020.9127357","mag":"3039852820"},"language":"en","primary_location":{"id":"doi:10.1109/percom45495.2020.9127357","is_oa":false,"landing_page_url":"https://doi.org/10.1109/percom45495.2020.9127357","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Pervasive Computing and Communications (PerCom)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5058990964","display_name":"Sebastian Soller","orcid":"https://orcid.org/0000-0002-2043-1110"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sebastian Soller","raw_affiliation_strings":["Almanara Research GmbH, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Almanara Research GmbH, Germany","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053491366","display_name":"Gerold H\u00f6lzl","orcid":null},"institutions":[{"id":"https://openalex.org/I186354981","display_name":"University of Passau","ror":"https://ror.org/05ydjnb78","country_code":"DE","type":"education","lineage":["https://openalex.org/I186354981"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Gerold Holzl","raw_affiliation_strings":["Chair of Embedded Systems, University of Passau, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chair of Embedded Systems, University of Passau, Germany","institution_ids":["https://openalex.org/I186354981"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5047807251","display_name":"Matthias Kranz","orcid":"https://orcid.org/0000-0003-3835-4974"},"institutions":[{"id":"https://openalex.org/I186354981","display_name":"University of Passau","ror":"https://ror.org/05ydjnb78","country_code":"DE","type":"education","lineage":["https://openalex.org/I186354981"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Matthias Kranz","raw_affiliation_strings":["Chair of Embedded Systems, University of Passau, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chair of Embedded Systems, University of Passau, Germany","institution_ids":["https://openalex.org/I186354981"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.345,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.57332745,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"9"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12761","display_name":"Data Stream Mining Techniques","score":0.9973000288009644,"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/T12761","display_name":"Data Stream Mining Techniques","score":0.9973000288009644,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9912999868392944,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9749000072479248,"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/downtime","display_name":"Downtime","score":0.9050800800323486},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7160845994949341},{"id":"https://openalex.org/keywords/alarm","display_name":"ALARM","score":0.5984703302383423},{"id":"https://openalex.org/keywords/production-line","display_name":"Production line","score":0.49051111936569214},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.47736096382141113},{"id":"https://openalex.org/keywords/machine-to-machine","display_name":"Machine to machine","score":0.4340245723724365},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3944014012813568},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.38079357147216797},{"id":"https://openalex.org/keywords/reliability-engineering","display_name":"Reliability engineering","score":0.3459964394569397},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.33829763531684875},{"id":"https://openalex.org/keywords/internet-of-things","display_name":"Internet of Things","score":0.23375743627548218},{"id":"https://openalex.org/keywords/embedded-system","display_name":"Embedded system","score":0.23150596022605896},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1478790044784546},{"id":"https://openalex.org/keywords/operating-system","display_name":"Operating system","score":0.12324696779251099}],"concepts":[{"id":"https://openalex.org/C180591934","wikidata":"https://www.wikidata.org/wiki/Q1253369","display_name":"Downtime","level":2,"score":0.9050800800323486},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7160845994949341},{"id":"https://openalex.org/C2779119184","wikidata":"https://www.wikidata.org/wiki/Q294350","display_name":"ALARM","level":2,"score":0.5984703302383423},{"id":"https://openalex.org/C99862985","wikidata":"https://www.wikidata.org/wiki/Q10858068","display_name":"Production line","level":2,"score":0.49051111936569214},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.47736096382141113},{"id":"https://openalex.org/C76763059","wikidata":"https://www.wikidata.org/wiki/Q251072","display_name":"Machine to machine","level":3,"score":0.4340245723724365},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3944014012813568},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.38079357147216797},{"id":"https://openalex.org/C200601418","wikidata":"https://www.wikidata.org/wiki/Q2193887","display_name":"Reliability engineering","level":1,"score":0.3459964394569397},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.33829763531684875},{"id":"https://openalex.org/C81860439","wikidata":"https://www.wikidata.org/wiki/Q251212","display_name":"Internet of Things","level":2,"score":0.23375743627548218},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.23150596022605896},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1478790044784546},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.12324696779251099},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/percom45495.2020.9127357","is_oa":false,"landing_page_url":"https://doi.org/10.1109/percom45495.2020.9127357","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Pervasive Computing and Communications (PerCom)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.6000000238418579}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W1501062552","https://openalex.org/W1574680346","https://openalex.org/W1927233378","https://openalex.org/W1967214670","https://openalex.org/W1984672166","https://openalex.org/W2010657328","https://openalex.org/W2014939786","https://openalex.org/W2039240409","https://openalex.org/W2040975718","https://openalex.org/W2075966418","https://openalex.org/W2087763371","https://openalex.org/W2097575504","https://openalex.org/W2111075016","https://openalex.org/W2114131343","https://openalex.org/W2128873747","https://openalex.org/W2145038434","https://openalex.org/W2289463038","https://openalex.org/W2401334501","https://openalex.org/W2599557761","https://openalex.org/W2736064258","https://openalex.org/W2789444712","https://openalex.org/W2918916311","https://openalex.org/W6713050680"],"related_works":["https://openalex.org/W2046276983","https://openalex.org/W2954002293","https://openalex.org/W2078264086","https://openalex.org/W4285200766","https://openalex.org/W4214827973","https://openalex.org/W2124941321","https://openalex.org/W4251454458","https://openalex.org/W1970287070","https://openalex.org/W2080951167","https://openalex.org/W1658823922"],"abstract_inverted_index":{"We":[0,11,95],"present":[1],"a":[2,13,23,69,75,97,144,150],"dynamic":[3,76],"error":[4,20],"prediction":[5],"system":[6,79,126,201,225],"for":[7,22],"industrial":[8,37,152],"production":[9,24,92,101,124],"machines.":[10],"implemented":[12,96],"flexible":[14],"data":[15,146,156],"collection":[16],"tool":[17],"to":[18,28,71,83,104,118,154,165,199,204,229],"create":[19],"warnings":[21],"line,":[25],"which":[26,61],"aims":[27],"improve":[29],"the":[30,51,91,105,123,130,182,200,209,214,218,224,227,231],"already":[31],"existing":[32],"static":[33],"alarm":[34,42],"models.":[35],"For":[36,53,213],"machines,":[38],"there":[39],"are":[40,62],"threshold-based":[41],"models":[43],"set":[44],"by":[45,111,137],"prior":[46],"experiences":[47],"and":[48,58,73,77,89,129,142,160,174,194,234],"observations":[49],"of":[50,107,216,223],"operator.":[52],"machines":[54,102],"without":[55],"standardized":[56],"interfaces":[57],"communication":[59],"protocols,":[60],"not":[63],"Industry":[64],"4.0":[65],"compatible,":[66],"it":[67],"represents":[68],"challenge":[70],"implement":[72],"add":[74],"opportunistic":[78],"behavior.":[80,169,212],"Machines":[81],"need":[82],"learn":[84],"from":[85],"past":[86],"errors":[87,203],"autonomously":[88],"adapt":[90],"properties":[93],"dynamically.":[94],"framework":[98],"that":[99],"makes":[100],"conform":[103],"Internet":[106],"Things":[108],"(IoT)":[109],"concepts,":[110],"making":[112],"previously":[113],"non-IoT":[114],"enabled":[115],"resources":[116],"available":[117],"get":[119,205],"new":[120,206],"insights":[121,207],"into":[122,208],"processes.The":[125],"component":[127],"recognition":[128],"database":[131],"setup":[132],"is":[133,226],"done":[134],"fully":[135],"automatically":[136],"our":[138],"developed":[139],"system.We":[140],"designed":[141],"applied":[143],"feature-based":[145],"drift":[147,171],"model":[148],"in":[149,163],"real-world":[151],"setting":[153],"determine":[155],"deviation":[157],"between":[158],"normal":[159],"erroneous":[161,168,178],"work-pieces":[162,176],"real-time":[164],"predict":[166],"upcoming":[167],"The":[170,190],"analysis":[172],"flagged":[173,192],"predicted":[175],"as":[177],"several":[179],"minutes":[180],"before":[181],"pre-defined":[183],"machine":[184,211],"alarms":[185],"would":[186],"have":[187],"been":[188],"raised.":[189],"resulting":[191],"sensors":[193],"values":[195],"can":[196],"be":[197],"compared":[198],"determined":[202],"abnormal":[210],"reduction":[215],"downtime,":[217],"most":[219],"valuable":[220],"immediate":[221],"result":[222],"ability":[228],"notify":[230],"operator":[232],"earlier":[233],"reduce":[235],"overall":[236],"downtime.":[237]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}