{"id":"W4395685145","doi":"10.18280/ijsse.140230","title":"Enhancing Remaining Useful Life Predictions in Predictive Maintenance of MOSFETs: The Efficacy of Integrated Particle Filter-Gaussian Process Regression Models","year":2024,"lang":"en","type":"article","venue":"International Journal of Safety and Security Engineering","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Universiti Tenaga Nasional; Tenaga Nasional Berhad","keywords":"Particle filter; Kriging; Gaussian process; Process (computing); Regression; Regression analysis; Computer science; Reliability engineering; Gaussian; Statistics; Engineering; Artificial intelligence; Machine learning; Mathematics; Kalman filter; Physics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007800995,0.0006888965,0.0007084923,0.000332865,0.0001701418,0.0007146972,0.0006669549,0.0007285998,0.0004519568],"category_scores_gemma":[0.003002341,0.0002715212,0.0003714449,0.0002560711,0.0001973643,0.001001537,0.0003104702,0.000748115,0.0001757757],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003974868,"about_ca_system_score_gemma":0.0006137006,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01038402,"about_ca_topic_score_gemma":0.007790345,"domain_scores_codex":[0.9998325,0.00003482033,0.00000863174,0.00005040213,0.00005155641,0.00002209407],"domain_scores_gemma":[0.9989329,0.0006532386,0.00009427753,0.00006235335,0.0002333004,0.00002386352],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001637332,0.0001147531,0.002420205,0.00005547924,0.00004177223,0.00003920367,0.00003930235,0.9227079,0.003437993,0.001051031,0.0005621845,0.0693664],"study_design_scores_gemma":[0.000001795336,0.000009785188,0.000209574,0.000001342077,0.000003701545,0.000002079678,0.000001416156,0.9992827,0.0003053934,0.0001510627,0.0000297508,0.000001418698],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.203228,0.0007706571,0.7930482,0.0002876082,0.0001058348,0.00002574473,0.00007815546,0.001005839,0.001449866],"genre_scores_gemma":[0.9814773,0.0001578178,0.01751828,0.00003828119,0.00002496182,0.00001050936,0.00006419225,0.00003248201,0.000676264],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01038402,"threshold_uncertainty_score":0.02064717,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01147548466122019,"score_gpt":0.2423333771337919,"score_spread":0.2308578924725717,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}