{"id":"W2808496542","doi":"10.1109/tie.2018.2844805","title":"Multiscale Convolutional Neural Networks for Fault Diagnosis of Wind Turbine Gearbox","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Electronics","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":866,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Science Foundation of Hebei Province; National Natural Science Foundation of China","keywords":"Computer science; Convolutional neural network; Feature extraction; Artificial intelligence; Fault (geology); Pattern recognition (psychology); Pooling; Feature (linguistics); Feature learning; Deep learning; Turbine; Artificial neural network; Machine learning; Engineering","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":[0.0002228944,0.0004488257,0.0002505859,0.0004294258,0.00009938721,0.000202345,0.0003664047,0.0003656941,0.0005871259],"category_scores_gemma":[0.0006183927,0.0001473872,0.0002692752,0.0002300292,0.0001432331,0.0004099737,0.0002823879,0.0002864835,0.000114199],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003816785,"about_ca_system_score_gemma":0.0002568428,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003807568,"about_ca_topic_score_gemma":0.005228033,"domain_scores_codex":[0.9999107,0.00001048799,0.000006004454,0.00002378144,0.00003477112,0.00001422928],"domain_scores_gemma":[0.9998859,0.00003778457,0.00002326724,0.00001503518,0.000032132,0.000005812877],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001932203,0.00007060598,0.004423933,0.0001265466,0.0000824897,0.0001982636,0.00005014092,0.456594,0.06862641,0.003669278,0.001347561,0.4646175],"study_design_scores_gemma":[0.000001676083,0.00001755256,0.0009401669,0.000003121547,0.000009162554,0.00001894245,0.000003098475,0.9939248,0.004320053,0.0005197366,0.0002388892,0.000002830685],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1339823,0.001070259,0.8618157,0.0001612308,0.00005471115,0.00003133941,0.0001480371,0.001115582,0.00162076],"genre_scores_gemma":[0.9191979,0.0003097391,0.07912332,0.00003965715,0.00002004499,0.00002058921,0.0001487802,0.00001647644,0.001123512],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003807568,"threshold_uncertainty_score":0.007570803,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02114578237884285,"score_gpt":0.2700611907667092,"score_spread":0.2489154083878664,"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."}}