{"id":"W2387352252","doi":"10.1177/0309524x16647842","title":"Condition monitoring and fault diagnosis of a small permanent magnet generator","year":2016,"lang":"en","type":"article","venue":"Wind Engineering","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Vibration; Condition monitoring; Wind power; Fault (geology); Wavelet; Turbine; Rotor (electric); Permanent magnet synchronous generator; Engineering; Fault detection and isolation; Magnet; Automotive engineering; Wavelet transform; Continuous wavelet transform; Computer science; Discrete wavelet transform; Acoustics; Mechanical engineering; Electrical engineering; Actuator","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":[0.00007957412,0.0001883937,0.0001964457,0.0001424351,0.00001796771,0.00001711334,0.00009872216,0.00008598335,0.00003855693],"category_scores_gemma":[0.00004394248,0.0001617548,0.00004129644,0.0000894245,0.00001705988,0.0001287476,0.00003691455,0.00008553826,0.000005909003],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005804993,"about_ca_system_score_gemma":0.000003822682,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001469279,"about_ca_topic_score_gemma":0.0000015971,"domain_scores_codex":[0.9992726,0.00000703889,0.0002281415,0.000156863,0.0001070532,0.0002282969],"domain_scores_gemma":[0.999586,0.000094863,0.00002381197,0.0001795277,0.00002573093,0.00009005471],"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":[0.000005660769,0.00005616183,0.1163721,0.0007661005,0.0001293258,0.00002744411,0.0003842734,0.02952997,0.8094811,0.0007159038,0.001376724,0.04115531],"study_design_scores_gemma":[0.0004777027,0.00007239896,0.07006472,0.0005469036,0.00003508151,0.00001576727,0.00001450029,0.01204034,0.911981,0.00005568808,0.004240336,0.0004555144],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9948858,0.0008595453,0.003079495,0.00004747325,0.0002413184,0.0001463031,0.00003499268,0.0005615607,0.0001434936],"genre_scores_gemma":[0.9932237,0.0007800368,0.005641631,0.000003928087,0.0001851026,0.00009664585,0.000003650738,0.00005337756,0.00001197146],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1025,"threshold_uncertainty_score":0.6596169,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008059902277710841,"score_gpt":0.2254300225980563,"score_spread":0.2173701203203455,"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."}}