{"id":"https://openalex.org/W2887286772","doi":"https://doi.org/10.23919/acc.2018.8431901","title":"Predictive Maintenance for Supermarket Refrigeration Systems Using Only Case Temperature Data","display_name":"Predictive Maintenance for Supermarket Refrigeration Systems Using Only Case Temperature Data","publication_year":2018,"publication_date":"2018-06-01","ids":{"openalex":"https://openalex.org/W2887286772","doi":"https://doi.org/10.23919/acc.2018.8431901","mag":"2887286772"},"language":"en","primary_location":{"id":"doi:10.23919/acc.2018.8431901","is_oa":false,"landing_page_url":"https://doi.org/10.23919/acc.2018.8431901","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 Annual American Control Conference (ACC)","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/A5017427183","display_name":"Kedar M. Kulkarni","orcid":"https://orcid.org/0000-0003-4685-6114"},"institutions":[{"id":"https://openalex.org/I4210103279","display_name":"IBM Research - India","ror":"https://ror.org/014wt7r80","country_code":"IN","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210103279","https://openalex.org/I4210114115"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Kedar Kulkarni","raw_affiliation_strings":["IBM Research, Bangalore, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM Research, Bangalore, India","institution_ids":["https://openalex.org/I4210103279"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050081788","display_name":"UmaMaheswari C. Devi","orcid":"https://orcid.org/0009-0001-3146-4313"},"institutions":[{"id":"https://openalex.org/I4210103279","display_name":"IBM Research - India","ror":"https://ror.org/014wt7r80","country_code":"IN","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210103279","https://openalex.org/I4210114115"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Umamaheswari Devi","raw_affiliation_strings":["IBM Research, Bangalore, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM Research, Bangalore, India","institution_ids":["https://openalex.org/I4210103279"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066378084","display_name":"Amith Sirighee","orcid":null},"institutions":[{"id":"https://openalex.org/I4210103279","display_name":"IBM Research - India","ror":"https://ror.org/014wt7r80","country_code":"IN","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210103279","https://openalex.org/I4210114115"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Amith Sirighee","raw_affiliation_strings":["IBM Research, Bangalore, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM Research, Bangalore, India","institution_ids":["https://openalex.org/I4210103279"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020415815","display_name":"Jagabondhu Hazra","orcid":"https://orcid.org/0000-0002-6372-0342"},"institutions":[{"id":"https://openalex.org/I4210103279","display_name":"IBM Research - India","ror":"https://ror.org/014wt7r80","country_code":"IN","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210103279","https://openalex.org/I4210114115"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Jagabondhu Hazra","raw_affiliation_strings":["IBM Research, Bangalore, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM Research, Bangalore, India","institution_ids":["https://openalex.org/I4210103279"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5037098189","display_name":"P. Siva Subba Rao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Praveen Rao","raw_affiliation_strings":["IBM Global Business Services, Milwaukee, WI, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM Global Business Services, Milwaukee, WI, USA","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.9929,"has_fulltext":false,"cited_by_count":44,"citation_normalized_percentile":{"value":0.92439549,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"4640","last_page":"4645"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9997000098228455,"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"}},"topics":[{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9997000098228455,"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"}},{"id":"https://openalex.org/T11667","display_name":"Advanced Chemical Sensor Technologies","score":0.982699990272522,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical 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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9815999865531921,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/refrigeration","display_name":"Refrigeration","score":0.6963770985603333},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6333196759223938},{"id":"https://openalex.org/keywords/dynamic-time-warping","display_name":"Dynamic time warping","score":0.48951613903045654},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4466201066970825},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4387859106063843},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.433197557926178},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4248078465461731},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.421808123588562},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.35614287853240967},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.18021160364151}],"concepts":[{"id":"https://openalex.org/C69907114","wikidata":"https://www.wikidata.org/wiki/Q747713","display_name":"Refrigeration","level":2,"score":0.6963770985603333},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6333196759223938},{"id":"https://openalex.org/C88516994","wikidata":"https://www.wikidata.org/wiki/Q1268863","display_name":"Dynamic time warping","level":2,"score":0.48951613903045654},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4466201066970825},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4387859106063843},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.433197557926178},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4248078465461731},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.421808123588562},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35614287853240967},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.18021160364151},{"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:10.23919/acc.2018.8431901","is_oa":false,"landing_page_url":"https://doi.org/10.23919/acc.2018.8431901","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 Annual American Control Conference (ACC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W1571464741","https://openalex.org/W1987019926","https://openalex.org/W1999489246","https://openalex.org/W2003438283","https://openalex.org/W2046775022","https://openalex.org/W2047552081","https://openalex.org/W2057659149","https://openalex.org/W2077806872","https://openalex.org/W2079905813","https://openalex.org/W2082642045","https://openalex.org/W2086379178","https://openalex.org/W2089662726","https://openalex.org/W2098759488","https://openalex.org/W2101234009","https://openalex.org/W2107092366","https://openalex.org/W2108969329","https://openalex.org/W2121435781","https://openalex.org/W2286961273","https://openalex.org/W4251369602","https://openalex.org/W6634100185","https://openalex.org/W6672097136"],"related_works":["https://openalex.org/W2030799363","https://openalex.org/W2950183183","https://openalex.org/W2341338763","https://openalex.org/W2288425735","https://openalex.org/W2349923317","https://openalex.org/W2894081631","https://openalex.org/W2986063033","https://openalex.org/W2040439981","https://openalex.org/W2472888994","https://openalex.org/W1791724651"],"abstract_inverted_index":{"We":[0,49,126],"present":[1],"a":[2,59,65,102,144,155],"machine-learning":[3],"based":[4],"approach":[5,129,142],"for":[6],"early":[7],"detection":[8],"of":[9,45,67,114,146,150,157],"issues":[10],"emerging":[11],"in":[12,117],"refrigeration":[13,36,120,135],"and":[14,31,41,77,86,93,154],"cold-storage":[15],"systems":[16],"that":[17,69,107],"has":[18],"the":[19,35,46,54,110],"following":[20],"desirable":[21],"features:":[22],"1)":[23],"Minimal":[24],"sensor":[25],"dependencies:":[26],"only":[27],"requires":[28],"temperature":[29],"readings":[30],"defrost":[32,76],"state":[33],"from":[34,133,137],"cases":[37,136],"2)":[38],"high":[39,43],"precision,":[40],"3)":[42],"generalizability":[44],"learnt":[47],"model.":[48],"achieve":[50],"this":[51],"by":[52],"casting":[53],"time-series":[55,72],"prediction":[56],"problem":[57],"as":[58],"classification":[60],"problem,":[61],"wherein":[62],"we":[63],"craft":[64],"set":[66],"features":[68,97],"capture":[70],"key":[71],"characteristics":[73],"specific":[74],"to":[75,100],"operating":[78],"regimes.":[79],"Our":[80],"feature":[81],"extraction":[82],"employs":[83],"seasonality-trend":[84],"decomposition":[85],"pattern":[87],"learning":[88],"using":[89],"dynamic":[90],"time":[91,149],"warping":[92],"clustering.":[94],"The":[95,141],"extracted":[96],"are":[98],"used":[99],"learn":[101],"random":[103],"forest-based":[104],"binary":[105],"classifier":[106],"can":[108],"indicate":[109],"presence":[111],"or":[112],"absence":[113],"an":[115],"issue":[116],"any":[118,123],"given":[119,124],"case":[121],"at":[122],"time.":[125],"validate":[127],"our":[128],"on":[130,161],"real":[131],"data":[132],"2265":[134],"several":[138],"large":[139],"supermarkets.":[140],"achieves":[143],"precision":[145],"89%,":[147],"lead":[148],"approximately":[151],"seven":[152],"days,":[153],"recall":[156],"46%":[158],"when":[159],"evaluated":[160],"unseen":[162],"cases.":[163]},"counts_by_year":[{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":9},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":12},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":3}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
