{"id":"W4384697165","doi":"10.22215/etd/2023-15575","title":"Anomaly Detection in the Vibration of Wind Turbine Blades using Gaussian Process Regression","year":2023,"lang":"en","type":"dissertation","venue":"","topic":"Structural Health Monitoring Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Anomaly detection; Turbine; Turbine blade; Wind power; Kriging; Aeroelasticity; Vibration; Gaussian process; Wind speed; Anomaly (physics); Engineering; Parametric statistics; Focus (optics); Gaussian; Process (computing); Renewable energy; Structural engineering; Computer science; Aerodynamics; Artificial intelligence; Machine learning; Acoustics; Mechanical engineering; Aerospace engineering; Mathematics; Statistics; Meteorology; Geography","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":[0.0008646852,0.0003720264,0.0004015091,0.0004552942,0.0001391509,0.000395397,0.0004337762,0.0004719459,0.0004132623],"category_scores_gemma":[0.002422396,0.0001826015,0.0004696496,0.0004174027,0.0002875257,0.0004398616,0.0003662821,0.0006706678,0.0002340622],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002220386,"about_ca_system_score_gemma":0.0003137486,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001785041,"about_ca_topic_score_gemma":0.001767491,"domain_scores_codex":[0.9996535,0.00009509529,0.0000129607,0.00009623102,0.0001079152,0.00003424319],"domain_scores_gemma":[0.9992548,0.0004617101,0.00007710286,0.00006335072,0.0001208358,0.00002224732],"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":[0.0003155723,0.0002232616,0.01085952,0.000120252,0.00009568191,0.0003169685,0.0003739631,0.5830113,0.08918571,0.005649736,0.0008207279,0.3090272],"study_design_scores_gemma":[0.000001584351,0.00002744745,0.001751605,0.000001982258,0.000003046045,0.00002492513,0.00001150482,0.9948989,0.002737269,0.0003985681,0.0001387002,0.00000450348],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1996142,0.000105712,0.7991843,0.0000826492,0.00001800191,0.00001930736,0.00005344775,0.0004997569,0.0004225604],"genre_scores_gemma":[0.8816521,0.0001297834,0.1167716,0.00001444874,0.00001499456,0.00002298115,0.0001656113,0.00005712308,0.001171501],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001785041,"threshold_uncertainty_score":0.004572928,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02530074359355758,"score_gpt":0.3324877595872239,"score_spread":0.3071870159936663,"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."}}