{"id":"https://openalex.org/W3211367073","doi":"https://doi.org/10.1145/3480001.3480015","title":"A Model for Pressure Sensor Health Diagnosis and Prediction Using a LSTM Algorithm","display_name":"A Model for Pressure Sensor Health Diagnosis and Prediction Using a LSTM Algorithm","publication_year":2021,"publication_date":"2021-07-23","ids":{"openalex":"https://openalex.org/W3211367073","doi":"https://doi.org/10.1145/3480001.3480015","mag":"3211367073"},"language":"en","primary_location":{"id":"doi:10.1145/3480001.3480015","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3480001.3480015","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 5th International Conference on Deep Learning Technologies (ICDLT)","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/A5100775727","display_name":"Zhihong Zhao","orcid":"https://orcid.org/0000-0002-4226-4915"},"institutions":[{"id":"https://openalex.org/I4210165204","display_name":"Zhuhai Institute of Advanced Technology","ror":"https://ror.org/05r1mzq61","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210145761","https://openalex.org/I4210165204"]},{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zhihong Zhao","raw_affiliation_strings":["Zhuhai Campus,Beijing Institute of Technology, China"],"affiliations":[{"raw_affiliation_string":"Zhuhai Campus,Beijing Institute of Technology, China","institution_ids":["https://openalex.org/I4210165204","https://openalex.org/I125839683"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088225868","display_name":"Tongyuan Bai","orcid":null},"institutions":[{"id":"https://openalex.org/I4210165204","display_name":"Zhuhai Institute of Advanced Technology","ror":"https://ror.org/05r1mzq61","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210145761","https://openalex.org/I4210165204"]},{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tongyuan Bai","raw_affiliation_strings":["Zhuhai Campus,Beijing Institute of Technology, China"],"affiliations":[{"raw_affiliation_string":"Zhuhai Campus,Beijing Institute of Technology, China","institution_ids":["https://openalex.org/I4210165204","https://openalex.org/I125839683"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5061330085","display_name":"Hanyu Huang","orcid":null},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]},{"id":"https://openalex.org/I4210165204","display_name":"Zhuhai Institute of Advanced Technology","ror":"https://ror.org/05r1mzq61","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210145761","https://openalex.org/I4210165204"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hanyu Huang","raw_affiliation_strings":["Zhuhai Campus,Beijing Institute of Technology, China"],"affiliations":[{"raw_affiliation_string":"Zhuhai Campus,Beijing Institute of Technology, China","institution_ids":["https://openalex.org/I4210165204","https://openalex.org/I125839683"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100775727"],"corresponding_institution_ids":["https://openalex.org/I125839683","https://openalex.org/I4210165204"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.16145971,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"24","issue":null,"first_page":"87","last_page":"92"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10876","display_name":"Fault Detection and Control Systems","score":0.998199999332428,"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":0.998199999332428,"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":0.996399998664856,"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/T11357","display_name":"Risk and Safety Analysis","score":0.9847999811172485,"subfield":{"id":"https://openalex.org/subfields/1804","display_name":"Statistics, Probability and Uncertainty"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/correctness","display_name":"Correctness","score":0.8545411825180054},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6879289746284485},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5699520111083984},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5024611949920654},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4557402729988098},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.4216890335083008},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.36625388264656067},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.08535778522491455}],"concepts":[{"id":"https://openalex.org/C55439883","wikidata":"https://www.wikidata.org/wiki/Q360812","display_name":"Correctness","level":2,"score":0.8545411825180054},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6879289746284485},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5699520111083984},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5024611949920654},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4557402729988098},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4216890335083008},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.36625388264656067},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.08535778522491455}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3480001.3480015","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3480001.3480015","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 5th International Conference on Deep Learning Technologies (ICDLT)","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":7,"referenced_works":["https://openalex.org/W1967372803","https://openalex.org/W2591055632","https://openalex.org/W2773549135","https://openalex.org/W2912454129","https://openalex.org/W2961928979","https://openalex.org/W2975966063","https://openalex.org/W2978540646"],"related_works":["https://openalex.org/W1667647204","https://openalex.org/W2404647514","https://openalex.org/W4247536566","https://openalex.org/W4241418540","https://openalex.org/W2018477250","https://openalex.org/W3119814709","https://openalex.org/W1508895727","https://openalex.org/W2725786787","https://openalex.org/W1590965489","https://openalex.org/W1875930651"],"abstract_inverted_index":{"To":[0],"increase":[1],"plant":[2,7,129],"equipment":[3,20,130],"efficiency":[4],"and":[5,50,86,121,125],"reduce":[6],"operation":[8],"costs,":[9],"as":[10,12],"well":[11],"to":[13,100,116],"address":[14],"the":[15,43,48,52,55,63,67,81,102,106,111,114],"problem":[16],"of":[17,47,54,74,78,84,89,91,105],"industrial":[18],"critical":[19],"health":[21,135],"diagnosis,":[22],"we":[23],"suggest":[24],"developing":[25],"a":[26,71,76,87],"deep":[27],"learning-based":[28],"LSTM":[29,68],"model":[30,49,56,69,97,112],"by":[31,61],"merging":[32],"data":[33,64],"from":[34],"pressure":[35,107],"sensors":[36],"while":[37],"they":[38],"are":[39],"operational.":[40],"By":[41],"modifying":[42],"learning":[44,72],"rate":[45,73],"parameter":[46,59],"judging":[51],"correctness":[53],"with":[57,70,118,134],"different":[58],"values":[60],"integrating":[62],"after":[65],"pre-processing,":[66],"0.006,":[75],"number":[77,88],"nodes":[79],"in":[80,132],"hidden":[82],"layer":[83],"8,":[85],"neurons":[90],"2":[92],"was":[93],"selected.":[94],"The":[95],"ideal":[96],"is":[98],"used":[99],"predict":[101,117],"remaining":[103],"life":[104],"sensor,":[108],"demonstrating":[109],"that":[110],"has":[113],"ability":[115],"high":[119],"accuracy":[120],"provides":[122],"effective":[123],"strategies":[124],"maintenance":[126],"plans":[127],"for":[128],"inspection":[131],"conjunction":[133],"degradation":[136],"photos.":[137]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
