{"id":"W2902372910","doi":"10.3390/en11123318","title":"Prediction of Remaining Useful Life of Wind Turbine Bearings under Non-Stationary Operating Conditions","year":2018,"lang":"en","type":"article","venue":"Energies","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"National Natural Science Foundation of China","keywords":"Turbine; Wind power; Computer science; Drivetrain; Support vector machine; Reliability (semiconductor); Vibration; Condition monitoring; Artificial neural network; Interval (graph theory); Process (computing); Noise (video); Bearing (navigation); Reliability engineering; Automotive engineering; Engineering; Power (physics); Artificial intelligence; Torque; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001117272,0.00009907544,0.0001579004,0.0001338825,0.00005413967,0.0000106064,0.00009017737,0.00005938088,0.00009606723],"category_scores_gemma":[0.00009254505,0.0001033794,0.00003455134,0.0001557101,0.00009243877,0.0002009332,0.00003604227,0.00007993165,0.000002680709],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002318192,"about_ca_system_score_gemma":0.00002160777,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007242146,"about_ca_topic_score_gemma":0.00001319312,"domain_scores_codex":[0.9993417,0.00001417302,0.0002959232,0.0001040061,0.0001354237,0.0001088213],"domain_scores_gemma":[0.9995433,0.00009575447,0.00006067881,0.0001613174,0.0001074359,0.00003150905],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000007625233,0.00007701288,0.03854296,0.0002425595,0.0001716958,0.000001541772,0.003239292,0.5594899,0.3735654,0.006294732,0.01802032,0.0003470028],"study_design_scores_gemma":[0.0003935104,0.0002175079,0.2771638,0.0003485504,0.00003961172,0.000004277132,0.0006298139,0.08827648,0.6311684,0.0007834056,0.0007607755,0.0002139063],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.99173,0.00006903412,0.002631386,0.00004144146,0.0001290181,0.00008419849,0.00005725382,0.0002728305,0.004984807],"genre_scores_gemma":[0.9921129,0.00003310512,0.007530125,0.00003424352,0.0001407854,0.00001756015,0.00006062906,0.00002578284,0.00004485497],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4712134,"threshold_uncertainty_score":0.4215689,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0172757339041748,"score_gpt":0.2630667565656356,"score_spread":0.2457910226614607,"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."}}