{"id":"W2049197830","doi":"10.1109/jsen.2014.2362411","title":"MEMS Multisensor Intelligent Damage Detection for Wind Turbines","year":2014,"lang":"en","type":"article","venue":"IEEE Sensors Journal","topic":"Structural Health Monitoring Techniques","field":"Engineering","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Structural health monitoring; Turbine; Condition monitoring; Wind power; Turbine blade; Sensor fusion; Finite element method; Maintenance engineering; Reliability engineering; Computer science; Vibration; Engineering; Damages; Structural engineering; Mechanical engineering; Artificial intelligence; Acoustics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001676659,0.0002047775,0.0002041607,0.0002077761,0.0001460223,0.0001664622,0.0002300777,0.0004011765,0.0006182867],"category_scores_gemma":[0.0003178065,0.0001418216,0.0001709958,0.0001316635,0.0001530391,0.0003553941,0.000189811,0.0001672407,0.0001293727],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002186057,"about_ca_system_score_gemma":0.0001030094,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004277381,"about_ca_topic_score_gemma":0.001059614,"domain_scores_codex":[0.9998778,0.00002205768,0.000004109479,0.00001887604,0.00007173312,0.000005399459],"domain_scores_gemma":[0.9999149,0.00002680261,0.00001617668,0.00001475282,0.00002331858,0.000004002383],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002524357,0.00008500661,0.00528428,0.0002549601,0.00005682433,0.0002862032,0.0001639395,0.1069508,0.6961454,0.003645998,0.002689161,0.184185],"study_design_scores_gemma":[0.00001074755,0.0002633659,0.008502858,0.00001741026,0.00002134563,0.0001621058,0.00005710182,0.8796427,0.1059612,0.001413275,0.003924699,0.00002313756],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3535539,0.002489025,0.6357305,0.0005626088,0.000245903,0.00009106712,0.0001952783,0.001166923,0.005964837],"genre_scores_gemma":[0.9463893,0.0003125159,0.05170393,0.0000580261,0.00002679319,0.00003547863,0.00006059571,0.0000101729,0.001403285],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0006182867,"threshold_uncertainty_score":0.002068341,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02298839524889656,"score_gpt":0.2850091530907495,"score_spread":0.262020757841853,"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."}}