{"id":"https://openalex.org/W3092046242","doi":"https://doi.org/10.1109/case48305.2020.9216855","title":"Anomaly detection and prediction in discrete manufacturing based on cooperative LSTM networks","display_name":"Anomaly detection and prediction in discrete manufacturing based on cooperative LSTM networks","publication_year":2020,"publication_date":"2020-08-01","ids":{"openalex":"https://openalex.org/W3092046242","doi":"https://doi.org/10.1109/case48305.2020.9216855","mag":"3092046242"},"language":"en","primary_location":{"id":"doi:10.1109/case48305.2020.9216855","is_oa":false,"landing_page_url":"https://doi.org/10.1109/case48305.2020.9216855","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE 16th International Conference on Automation Science and Engineering (CASE)","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/A5105421416","display_name":"B. Lindemann","orcid":null},"institutions":[{"id":"https://openalex.org/I100066346","display_name":"University of Stuttgart","ror":"https://ror.org/04vnq7t77","country_code":"DE","type":"education","lineage":["https://openalex.org/I100066346"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Benjamin Lindemann","raw_affiliation_strings":["Institute of Industrial Automation and Software Engineering at University of Stuttgart, Stuttgart, Germany"],"affiliations":[{"raw_affiliation_string":"Institute of Industrial Automation and Software Engineering at University of Stuttgart, Stuttgart, Germany","institution_ids":["https://openalex.org/I100066346"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042926074","display_name":"Nasser Jazdi","orcid":"https://orcid.org/0000-0001-6722-0911"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nasser Jazdi","raw_affiliation_strings":["Institute of Industrial Automation and Software Engineering at University, Stuttgart"],"affiliations":[{"raw_affiliation_string":"Institute of Industrial Automation and Software Engineering at University, Stuttgart","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5072168528","display_name":"Michael Weyrich","orcid":"https://orcid.org/0000-0003-3176-9288"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Michael Weyrich","raw_affiliation_strings":["Institute of Industrial Automation and Software Engineering at University, Stuttgart"],"affiliations":[{"raw_affiliation_string":"Institute of Industrial Automation and Software Engineering at University, Stuttgart","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5105421416"],"corresponding_institution_ids":["https://openalex.org/I100066346"],"apc_list":null,"apc_paid":null,"fwci":4.2972,"has_fulltext":false,"cited_by_count":42,"citation_normalized_percentile":{"value":0.94758327,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1003","last_page":"1010"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10876","display_name":"Fault Detection and Control Systems","score":0.9958000183105469,"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":0.9958000183105469,"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/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9937999844551086,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing 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/T10188","display_name":"Advanced machining processes and optimization","score":0.9790999889373779,"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/anomaly-detection","display_name":"Anomaly detection","score":0.7885376811027527},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6506922841072083},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5899683237075806},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.5854803323745728},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.5339734554290771},{"id":"https://openalex.org/keywords/sequence","display_name":"Sequence (biology)","score":0.5057988166809082},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5011739730834961},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4871325194835663},{"id":"https://openalex.org/keywords/automotive-industry","display_name":"Automotive industry","score":0.4870441257953644},{"id":"https://openalex.org/keywords/discrete-manufacturing","display_name":"Discrete manufacturing","score":0.44674715399742126},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.4365460276603699},{"id":"https://openalex.org/keywords/stability","display_name":"Stability (learning theory)","score":0.4244173765182495},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.4146636724472046},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3528887927532196},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.32826125621795654},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.2278842329978943},{"id":"https://openalex.org/keywords/production","display_name":"Production (economics)","score":0.18327006697654724}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.7885376811027527},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6506922841072083},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5899683237075806},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.5854803323745728},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.5339734554290771},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.5057988166809082},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5011739730834961},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4871325194835663},{"id":"https://openalex.org/C526921623","wikidata":"https://www.wikidata.org/wiki/Q190117","display_name":"Automotive industry","level":2,"score":0.4870441257953644},{"id":"https://openalex.org/C2780720960","wikidata":"https://www.wikidata.org/wiki/Q5282052","display_name":"Discrete manufacturing","level":3,"score":0.44674715399742126},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.4365460276603699},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.4244173765182495},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.4146636724472046},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3528887927532196},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.32826125621795654},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.2278842329978943},{"id":"https://openalex.org/C2778348673","wikidata":"https://www.wikidata.org/wiki/Q739302","display_name":"Production (economics)","level":2,"score":0.18327006697654724},{"id":"https://openalex.org/C54355233","wikidata":"https://www.wikidata.org/wiki/Q7162","display_name":"Genetics","level":1,"score":0.0},{"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/C139719470","wikidata":"https://www.wikidata.org/wiki/Q39680","display_name":"Macroeconomics","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/case48305.2020.9216855","is_oa":false,"landing_page_url":"https://doi.org/10.1109/case48305.2020.9216855","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE 16th International Conference on Automation Science and Engineering (CASE)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.550000011920929}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W2064675550","https://openalex.org/W2107200351","https://openalex.org/W2122646361","https://openalex.org/W2130942839","https://openalex.org/W2530867817","https://openalex.org/W2531908596","https://openalex.org/W2545177271","https://openalex.org/W2581522324","https://openalex.org/W2681964327","https://openalex.org/W2735778951","https://openalex.org/W2753352458","https://openalex.org/W2754051771","https://openalex.org/W2796013264","https://openalex.org/W2801617672","https://openalex.org/W2885191650","https://openalex.org/W2889351375","https://openalex.org/W2900555793","https://openalex.org/W2922299424","https://openalex.org/W2936131980","https://openalex.org/W2951362784","https://openalex.org/W2955963731","https://openalex.org/W2962965465","https://openalex.org/W2982252459","https://openalex.org/W2997070389","https://openalex.org/W3158551399","https://openalex.org/W4297814361","https://openalex.org/W6638205174","https://openalex.org/W6679436768","https://openalex.org/W6720514713","https://openalex.org/W6728698659"],"related_works":["https://openalex.org/W2902199098","https://openalex.org/W2042251007","https://openalex.org/W2227316993","https://openalex.org/W1629725936","https://openalex.org/W2510887268","https://openalex.org/W2956106163","https://openalex.org/W2984111956","https://openalex.org/W2884624803","https://openalex.org/W2775793919","https://openalex.org/W3092046242"],"abstract_inverted_index":{"Manufacturing":[0],"processes":[1],"are":[2,14,27,150],"characterized":[3],"by":[4,129,175],"their":[5],"temporal":[6],"and":[7,17,40,89,114,127,145,161],"spatial":[8],"distributed":[9],"nonlinear":[10],"physics.":[11],"Analytical":[12],"models":[13,19,85,149],"not":[15,21,28,95],"available":[16],"numerical":[18],"do":[20,94],"incorporate":[22],"abnormal":[23],"process":[24,38,88,125],"effects":[25],"that":[26],"known":[29],"to":[30,122],"the":[31,46,61,80,107,146],"engineer.":[32],"These":[33],"unknown":[34],"anomalies":[35,128],"cause":[36],"reduced":[37],"stability":[39],"fluctuant":[41],"product":[42],"quality.":[43],"To":[44],"tackle":[45],"problem,":[47],"numerous":[48],"approaches":[49,77],"for":[50,73,86,99,159,163,181,189],"anomaly":[51,90,137],"detection":[52,113],"based":[53,117,151],"on":[54,118,152],"neural":[55],"networks":[56,67,154],"have":[57,68],"been":[58,70],"developed":[59],"over":[60],"years.":[62],"Long":[63],"short-term":[64,100,160],"memory":[65],"(LSTM)":[66],"also":[69],"investigated":[71],"intensively":[72],"prediction":[74,84,115,148],"purposes.":[75],"Current":[76],"lack":[78],"in":[79],"capability":[81],"of":[82,177],"constructing":[83],"both":[87],"behavior.":[91],"Furthermore,":[92],"they":[93],"deliver":[96],"a":[97,111,119,171,178,185],"solution":[98],"as":[101,103,141],"well":[102],"long-term":[104,164],"anomalies.":[105],"Hence,":[106],"current":[108],"paper":[109],"presents":[110],"novel":[112],"procedure":[116],"LSTM":[120,143,162],"architecture":[121],"cooperatively":[123],"predict":[124],"outputs":[126],"using":[130],"two":[131],"separate":[132],"but":[133],"interacting":[134],"models.":[135],"The":[136,166],"detector":[138],"is":[139,168],"realized":[140],"stacked":[142],"auto-encoder":[144],"cooperative":[147],"sequence-to-sequence":[153],"with":[155],"gated":[156],"recurrent":[157],"units":[158],"effects.":[165],"approach":[167],"evaluated":[169],"within":[170],"real":[172],"industrial":[173],"environment":[174],"means":[176],"production":[179],"plant":[180],"hot":[182],"forging":[183],"at":[184],"German":[186],"automotive":[187],"supplier":[188],"metal":[190],"components.":[191]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":7},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":10},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":12},{"year":2020,"cited_by_count":1}],"updated_date":"2026-03-03T08:47:05.690250","created_date":"2025-10-10T00:00:00"}
