{"id":"https://openalex.org/W2970918241","doi":"https://doi.org/10.1109/icphm.2019.8819392","title":"Generating Real-valued Failure Data for Prognostics Under the Conditions of Limited Data Availability","display_name":"Generating Real-valued Failure Data for Prognostics Under the Conditions of Limited Data Availability","publication_year":2019,"publication_date":"2019-06-01","ids":{"openalex":"https://openalex.org/W2970918241","doi":"https://doi.org/10.1109/icphm.2019.8819392","mag":"2970918241"},"language":"en","primary_location":{"id":"doi:10.1109/icphm.2019.8819392","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icphm.2019.8819392","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Prognostics and Health Management (ICPHM)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.17863/cam.38574","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5044772735","display_name":"Gishan Don Ranasinghe","orcid":"https://orcid.org/0000-0001-6658-3286"},"institutions":[{"id":"https://openalex.org/I241749","display_name":"University of Cambridge","ror":"https://ror.org/013meh722","country_code":"GB","type":"education","lineage":["https://openalex.org/I241749"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Gishan Don Ranasinghe","raw_affiliation_strings":["Institute for Manufacturing Department of Engineering, University of Cambridge, Cambridge, United Kingdom"],"affiliations":[{"raw_affiliation_string":"Institute for Manufacturing Department of Engineering, University of Cambridge, Cambridge, United Kingdom","institution_ids":["https://openalex.org/I241749"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5024191250","display_name":"Ajith Kumar Parlikad","orcid":"https://orcid.org/0000-0001-6214-1739"},"institutions":[{"id":"https://openalex.org/I241749","display_name":"University of Cambridge","ror":"https://ror.org/013meh722","country_code":"GB","type":"education","lineage":["https://openalex.org/I241749"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Ajith Kumar Parlikad","raw_affiliation_strings":["Institute for Manufacturing Department of Engineering, University of Cambridge, Cambridge, United Kingdom"],"affiliations":[{"raw_affiliation_string":"Institute for Manufacturing Department of Engineering, University of Cambridge, Cambridge, United Kingdom","institution_ids":["https://openalex.org/I241749"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5044772735"],"corresponding_institution_ids":["https://openalex.org/I241749"],"apc_list":null,"apc_paid":null,"fwci":1.3475,"has_fulltext":false,"cited_by_count":15,"citation_normalized_percentile":{"value":0.81409902,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.9932000041007996,"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/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.9932000041007996,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9927999973297119,"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":0.9886999726295471,"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/prognostics","display_name":"Prognostics","score":0.986407995223999},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5827990174293518},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.5709479451179504},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.5198636054992676},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.4858390986919403},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.47545191645622253},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4523128867149353},{"id":"https://openalex.org/keywords/reliability-engineering","display_name":"Reliability engineering","score":0.4039277732372284},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3697812557220459},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3578966557979584},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.3139772117137909}],"concepts":[{"id":"https://openalex.org/C129364497","wikidata":"https://www.wikidata.org/wiki/Q3042561","display_name":"Prognostics","level":2,"score":0.986407995223999},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5827990174293518},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.5709479451179504},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.5198636054992676},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4858390986919403},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.47545191645622253},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4523128867149353},{"id":"https://openalex.org/C200601418","wikidata":"https://www.wikidata.org/wiki/Q2193887","display_name":"Reliability engineering","level":1,"score":0.4039277732372284},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3697812557220459},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3578966557979584},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.3139772117137909},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/icphm.2019.8819392","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icphm.2019.8819392","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Prognostics and Health Management (ICPHM)","raw_type":"proceedings-article"},{"id":"pmh:oai:generic.eprints.org:1073147","is_oa":false,"landing_page_url":"http://publications.eng.cam.ac.uk/1073147/","pdf_url":null,"source":{"id":"https://openalex.org/S4406922847","display_name":"Cambridge University Engineering Department Publications Database","issn_l":null,"issn":null,"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":"","raw_type":"Conference or Workshop Item"},{"id":"pmh:oai:www.repository.cam.ac.uk:1810/291405","is_oa":false,"landing_page_url":"https://www.repository.cam.ac.uk/handle/1810/291405","pdf_url":null,"source":{"id":"https://openalex.org/S4306401777","display_name":"Apollo (University of Cambridge)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I241749","host_organization_name":"University of Cambridge","host_organization_lineage":["https://openalex.org/I241749"],"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":"Conference Object"},{"id":"doi:10.17863/cam.38574","is_oa":true,"landing_page_url":"https://doi.org/10.17863/cam.38574","pdf_url":null,"source":{"id":"https://openalex.org/S7407050737","display_name":"Apollo","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":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.17863/cam.38574","is_oa":true,"landing_page_url":"https://doi.org/10.17863/cam.38574","pdf_url":null,"source":{"id":"https://openalex.org/S7407050737","display_name":"Apollo","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":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[{"display_name":"Life in Land","score":0.6399999856948853,"id":"https://metadata.un.org/sdg/15"}],"awards":[{"id":"https://openalex.org/G2854561769","display_name":null,"funder_award_id":"EP/I019308/1","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"},{"id":"https://openalex.org/G6514736062","display_name":null,"funder_award_id":"EP/L010917/1","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"},{"id":"https://openalex.org/G6732657250","display_name":null,"funder_award_id":"EP/K000314/1","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"},{"id":"https://openalex.org/G6924865128","display_name":null,"funder_award_id":"EP/N021614/1","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"},{"id":"https://openalex.org/G7195739846","display_name":null,"funder_award_id":"1949527","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"}],"funders":[{"id":"https://openalex.org/F4320334627","display_name":"Engineering and Physical Sciences Research Council","ror":"https://ror.org/0439y7842"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W1965895350","https://openalex.org/W1982275278","https://openalex.org/W2045186954","https://openalex.org/W2099471712","https://openalex.org/W2125389028","https://openalex.org/W2131391419","https://openalex.org/W2171182814","https://openalex.org/W2522433306","https://openalex.org/W2523180702","https://openalex.org/W2588003421","https://openalex.org/W2623550831","https://openalex.org/W2793167745","https://openalex.org/W2889448589","https://openalex.org/W4285719527","https://openalex.org/W4320013936","https://openalex.org/W6678815747"],"related_works":["https://openalex.org/W2310476526","https://openalex.org/W3213192587","https://openalex.org/W2166537673","https://openalex.org/W4287630611","https://openalex.org/W3094960827","https://openalex.org/W3161989282","https://openalex.org/W3158596343","https://openalex.org/W4285322112","https://openalex.org/W4292794239","https://openalex.org/W4385572030"],"abstract_inverted_index":{"Data-driven":[0],"prognostics":[1,23,125,155],"solutions":[2],"underperform":[3],"under":[4],"the":[5,13,51,72,90,100,114,147,153,160,163],"conditions":[6],"of":[7,15,53,77,116],"limited":[8],"failure":[9,16,40,54,82,101],"data":[10,17,41,55,63,83],"availability":[11],"since":[12],"number":[14,52],"samples":[18,56],"is":[19,57,75,106,138],"insufficient":[20],"for":[21,37,162],"training":[22,44,149],"models":[24,126,143],"effectively.":[25],"In":[26,59],"order":[27],"to":[28,46,61,99,174],"address":[29],"this":[30,86],"problem,":[31],"we":[32,88],"present":[33],"a":[34,109,167],"novel":[35],"methodology":[36,74,105],"generating":[38,78],"real-valued":[39],"which":[42,66],"allows":[43],"datasets":[45],"be":[47],"augmented":[48,148],"so":[49],"that":[50,140],"increased.":[58],"contrast":[60],"existing":[62],"generation":[64],"techniques":[65],"duplicate":[67],"or":[68],"randomly":[69],"generate":[70],"data,":[71],"proposed":[73,104,158],"capable":[76],"new":[79],"and":[80,95,133,176],"realistic":[81],"samples.":[84],"To":[85],"end,":[87],"utilised":[89],"conditional":[91],"generative":[92],"adversarial":[93],"network":[94],"auxiliary":[96],"information":[97],"pertaining":[98],"modes.":[102],"The":[103],"evaluated":[107],"in":[108,121,159],"real-world":[110],"case":[111,164],"study":[112,165],"involving":[113],"prediction":[115],"air":[117],"purge":[118],"valve":[119],"failures":[120],"heavy":[122],"trucks.":[123],"Two":[124],"are":[127,144,179],"developed":[128],"using":[129],"gradient":[130],"boosting":[131],"machine":[132],"random":[134],"forest":[135],"classifiers.":[136],"It":[137],"shown":[139],"when":[141],"these":[142],"trained":[145],"on":[146],"dataset,":[150],"they":[151],"outperform":[152],"best":[154],"solution":[156],"previously":[157],"literature":[161],"by":[166,181],"large":[168],"margin.":[169],"More":[170],"specifically,":[171],"costs":[172],"due":[173],"breakdowns":[175],"false":[177],"alarms":[178],"reduced":[180],"44%.":[182]},"counts_by_year":[{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":1}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}
