{"id":"W2045790067","doi":"10.1016/j.ymssp.2014.03.006","title":"Damage detection method for wind turbine blades based on dynamics analysis and mode shape difference curvature information","year":2014,"lang":"en","type":"article","venue":"Mechanical Systems and Signal Processing","topic":"Structural Health Monitoring Techniques","field":"Engineering","cited_by":101,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Turbine blade; Finite element method; Structural engineering; Modal analysis; Aerodynamics; Lift (data mining); Turbine; Normal mode; Blade (archaeology); Curvature; Engineering; Modal; Acoustics; Computer science; Vibration; Mechanical engineering; Aerospace engineering; Materials science; Mathematics; Physics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001964988,0.0004312429,0.0004774859,0.001028222,0.00027054,0.0002632403,0.0003698877,0.0004697568,0.0007153241],"category_scores_gemma":[0.0004653168,0.0002399832,0.0003153188,0.0003830989,0.000189221,0.000570568,0.0003131017,0.0003410679,0.000283362],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001664176,"about_ca_system_score_gemma":0.0003346826,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006907863,"about_ca_topic_score_gemma":0.001538968,"domain_scores_codex":[0.9998143,0.00001416381,0.00001139802,0.00004471782,0.0001022935,0.00001308623],"domain_scores_gemma":[0.9996331,0.00007418779,0.00005477421,0.00002904584,0.0001806621,0.00002824018],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003739243,0.0001401177,0.008892101,0.0002386656,0.00006196283,0.0002233241,0.0001775076,0.02117865,0.4291986,0.001398495,0.001580926,0.5365358],"study_design_scores_gemma":[0.00003698137,0.0004255083,0.03417955,0.00002694571,0.00009362236,0.0008558066,0.00008503984,0.874451,0.08604907,0.001216805,0.002498834,0.00008094709],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1527814,0.0004997298,0.844843,0.00009251357,0.0000784782,0.0000640835,0.00009254754,0.0005904609,0.0009576469],"genre_scores_gemma":[0.7788537,0.0003879007,0.2179037,0.00005396972,0.0000598426,0.00006044732,0.0002365027,0.00003315805,0.002410792],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001028222,"threshold_uncertainty_score":0.002392948,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01079491307568693,"score_gpt":0.2711246177177527,"score_spread":0.2603297046420658,"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."}}