{"id":"https://openalex.org/W2919567172","doi":"https://doi.org/10.1109/tencon.2018.8650152","title":"A Generalized Predictive Framework for Data Driven Prognostics and Diagnostics using Machine Logs","display_name":"A Generalized Predictive Framework for Data Driven Prognostics and Diagnostics using Machine Logs","publication_year":2018,"publication_date":"2018-10-01","ids":{"openalex":"https://openalex.org/W2919567172","doi":"https://doi.org/10.1109/tencon.2018.8650152","mag":"2919567172"},"language":"en","primary_location":{"id":"doi:10.1109/tencon.2018.8650152","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tencon.2018.8650152","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"TENCON 2018 - 2018 IEEE Region 10 Conference","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/A5068002105","display_name":"Shili Xiang","orcid":"https://orcid.org/0000-0001-6598-2904"},"institutions":[{"id":"https://openalex.org/I115228651","display_name":"Agency for Science, Technology and Research","ror":"https://ror.org/036wvzt09","country_code":"SG","type":"government","lineage":["https://openalex.org/I115228651"]},{"id":"https://openalex.org/I3005327000","display_name":"Institute for Infocomm Research","ror":"https://ror.org/053rfa017","country_code":"SG","type":"facility","lineage":["https://openalex.org/I115228651","https://openalex.org/I3005327000","https://openalex.org/I91275662"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Shili Xiang","raw_affiliation_strings":["Institute for Infocomm Research, A*STAR Singapaore, Singapore"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute for Infocomm Research, A*STAR Singapaore, Singapore","institution_ids":["https://openalex.org/I3005327000","https://openalex.org/I115228651"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050270789","display_name":"Dong Huang","orcid":"https://orcid.org/0000-0003-3923-8828"},"institutions":[{"id":"https://openalex.org/I115228651","display_name":"Agency for Science, Technology and Research","ror":"https://ror.org/036wvzt09","country_code":"SG","type":"government","lineage":["https://openalex.org/I115228651"]},{"id":"https://openalex.org/I3005327000","display_name":"Institute for Infocomm Research","ror":"https://ror.org/053rfa017","country_code":"SG","type":"facility","lineage":["https://openalex.org/I115228651","https://openalex.org/I3005327000","https://openalex.org/I91275662"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Dong Huang","raw_affiliation_strings":["Institute for Infocomm Research, A*STAR Singapaore, Singapore"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute for Infocomm Research, A*STAR Singapaore, Singapore","institution_ids":["https://openalex.org/I3005327000","https://openalex.org/I115228651"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100418684","display_name":"Xiaoli Li","orcid":"https://orcid.org/0000-0002-0762-6562"},"institutions":[{"id":"https://openalex.org/I115228651","display_name":"Agency for Science, Technology and Research","ror":"https://ror.org/036wvzt09","country_code":"SG","type":"government","lineage":["https://openalex.org/I115228651"]},{"id":"https://openalex.org/I3005327000","display_name":"Institute for Infocomm Research","ror":"https://ror.org/053rfa017","country_code":"SG","type":"facility","lineage":["https://openalex.org/I115228651","https://openalex.org/I3005327000","https://openalex.org/I91275662"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Xiaoli Li","raw_affiliation_strings":["Institute for Infocomm Research, A*STAR Singapaore, Singapore"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute for Infocomm Research, A*STAR Singapaore, Singapore","institution_ids":["https://openalex.org/I3005327000","https://openalex.org/I115228651"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.124,"has_fulltext":false,"cited_by_count":20,"citation_normalized_percentile":{"value":0.80404528,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"0695","last_page":"0700"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10876","display_name":"Fault Detection and Control Systems","score":0.9984999895095825,"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":0.9984999895095825,"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/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.9930999875068665,"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":0.9621000289916992,"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/prognostics","display_name":"Prognostics","score":0.9182441830635071},{"id":"https://openalex.org/keywords/downtime","display_name":"Downtime","score":0.8453657627105713},{"id":"https://openalex.org/keywords/predictive-maintenance","display_name":"Predictive maintenance","score":0.7845754623413086},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6056632995605469},{"id":"https://openalex.org/keywords/reliability-engineering","display_name":"Reliability engineering","score":0.5380486845970154},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5321254730224609},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5307146310806274},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.48575499653816223},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.4851323068141937},{"id":"https://openalex.org/keywords/precision-and-recall","display_name":"Precision and recall","score":0.482231467962265},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.4738976061344147},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.45928123593330383},{"id":"https://openalex.org/keywords/fault-detection-and-isolation","display_name":"Fault detection and isolation","score":0.4579533338546753},{"id":"https://openalex.org/keywords/condition-monitoring","display_name":"Condition monitoring","score":0.41714417934417725},{"id":"https://openalex.org/keywords/big-data","display_name":"Big data","score":0.4115694761276245},{"id":"https://openalex.org/keywords/preventive-maintenance","display_name":"Preventive maintenance","score":0.4100399911403656},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.36048024892807007},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.3120931386947632},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.117034912109375}],"concepts":[{"id":"https://openalex.org/C129364497","wikidata":"https://www.wikidata.org/wiki/Q3042561","display_name":"Prognostics","level":2,"score":0.9182441830635071},{"id":"https://openalex.org/C180591934","wikidata":"https://www.wikidata.org/wiki/Q1253369","display_name":"Downtime","level":2,"score":0.8453657627105713},{"id":"https://openalex.org/C70452415","wikidata":"https://www.wikidata.org/wiki/Q3182448","display_name":"Predictive maintenance","level":2,"score":0.7845754623413086},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6056632995605469},{"id":"https://openalex.org/C200601418","wikidata":"https://www.wikidata.org/wiki/Q2193887","display_name":"Reliability engineering","level":1,"score":0.5380486845970154},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5321254730224609},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5307146310806274},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.48575499653816223},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.4851323068141937},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.482231467962265},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.4738976061344147},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.45928123593330383},{"id":"https://openalex.org/C152745839","wikidata":"https://www.wikidata.org/wiki/Q5438153","display_name":"Fault detection and isolation","level":3,"score":0.4579533338546753},{"id":"https://openalex.org/C2775846686","wikidata":"https://www.wikidata.org/wiki/Q643012","display_name":"Condition monitoring","level":2,"score":0.41714417934417725},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.4115694761276245},{"id":"https://openalex.org/C24090081","wikidata":"https://www.wikidata.org/wiki/Q1043452","display_name":"Preventive maintenance","level":2,"score":0.4100399911403656},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.36048024892807007},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.3120931386947632},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.117034912109375},{"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/C172707124","wikidata":"https://www.wikidata.org/wiki/Q423488","display_name":"Actuator","level":2,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","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/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tencon.2018.8650152","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tencon.2018.8650152","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"TENCON 2018 - 2018 IEEE Region 10 Conference","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5,"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W1768257245","https://openalex.org/W1981515531","https://openalex.org/W2024094473","https://openalex.org/W2045186954","https://openalex.org/W2048231652","https://openalex.org/W2088794999","https://openalex.org/W2133140849","https://openalex.org/W2135479611","https://openalex.org/W2152680124","https://openalex.org/W2160222246","https://openalex.org/W2207789808","https://openalex.org/W2320972650","https://openalex.org/W2911964244","https://openalex.org/W4233825451"],"related_works":["https://openalex.org/W1557405600","https://openalex.org/W4214827973","https://openalex.org/W1601861909","https://openalex.org/W2672304337","https://openalex.org/W2187904564","https://openalex.org/W3144562559","https://openalex.org/W2349635908","https://openalex.org/W2029157094","https://openalex.org/W2063493629","https://openalex.org/W2908973203"],"abstract_inverted_index":{"Malfunctions":[0],"in":[1,37,40,159,180,190,198,237],"machines":[2],"require":[3],"equipment":[4,23],"engineers":[5,27],"to":[6,85,117],"conduct":[7],"fault":[8,11,96],"diagnostic.":[9],"The":[10,138],"diagnostics":[12,97,114,230],"is":[13,110,167,197,210],"traditionally":[14],"reliant":[15],"on":[16,94],"the":[17,22,64,69,87,119,124,142,154,160,174,181,194,199,207,250,257],"skills":[18],"and":[19,25,35,43,72,98,115,122,151,206,242],"experiences":[20],"of":[21,63,80,177,201,239],"operators":[24],"maintenance":[26,70,88,120],"heavily,":[28],"which":[29],"creates":[30],"an":[31,78],"unnecessary":[32],"technical":[33],"barrier":[34],"results":[36,224],"extra":[38],"cost":[39,42,89,121],"downtime":[41],"operation":[44],"overhead.":[45],"Meanwhile,":[46],"there":[47],"have":[48,187,225],"been":[49,128,188,247],"rich":[50],"machine":[51,65,95,113],"logs":[52],"(sensory":[53],"readings,":[54],"performance":[55],"logs,":[56,58],"system":[57],"context":[59],"data,":[60],"process":[61],"data)":[62],"as":[66,68],"well":[67],"data":[71,76,107,133,143,164],"post-service":[73],"reports.":[74],"Such":[75],"provide":[77],"opportunity":[79],"leveraging":[81],"intelligent":[82],"data-driven":[83],"technologies":[84],"reduce":[86],"through":[90],"developing":[91],"automated":[92],"solutions":[93],"prognostic":[99],"for":[100,112,169,204,216,219],"critical":[101],"component":[102],"failures.In":[103],"this":[104,191],"paper,":[105],"a":[106,211],"driven":[108],"framework":[109,140,254],"proposed":[111,139,251],"prognostics":[116,214,253],"relieve":[118],"increase":[123],"efficiency.":[125],"It":[126,244],"has":[127,245],"validated":[129],"with":[130],"real-world":[131],"big":[132],"from":[134],"complex":[135],"vending":[136],"machines.":[137],"addresses":[141],"size":[144],"issue":[145],"effectively":[146],"by":[147],"deriving":[148],"applicable":[149],"features":[150],"subsequently":[152],"sustaining":[153],"top":[155],"attributable":[156],"ones":[157],"only":[158],"model.":[161],"An":[162],"accurate":[163],"labeling":[165],"methodology":[166],"developed":[168,189,229],"supervised":[170],"learning":[171],"via":[172],"comparing":[173],"serial":[175],"number":[176],"target":[178],"components":[179],"adjacent":[182],"dates.":[183],"Two":[184],"predictive":[185],"models":[186],"work":[192],"whereby":[193],"first":[195],"one":[196,209],"domain":[200],"binary":[202],"classification":[203,218],"diagnostics,":[205],"second":[208],"generalized":[212],"two-stage":[213,252],"model":[215,231],"multi-class":[217],"preventive":[220],"maintenance.":[221],"Cross-validated":[222],"simulation":[223],"shown":[226,248],"that":[227,249],"our":[228],"can":[232,255],"achieve":[233],"above":[234],"80%":[235],"accuracy":[236],"terms":[238],"precision,":[240],"recall,":[241],"F-measure.":[243],"also":[246],"outperform":[256],"conventional":[258],"one-stage":[259],"multiclass":[260],"prediction":[261],"models.":[262]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}