{"id":"W3149373559","doi":"10.3390/vibration4020019","title":"Exploring the Relationship between Preprocessing and Hyperparameter Tuning for Vibration-Based Machine Fault Diagnosis Using CNNs","year":2021,"lang":"en","type":"article","venue":"Vibration","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Hyperparameter; Overfitting; Computer science; Convolutional neural network; Artificial intelligence; Pattern recognition (psychology); Hyperparameter optimization; Kernel (algebra); Preprocessor; Machine learning; Spectrogram; Artificial neural network; Support vector machine; Mathematics","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":[0.001660351,0.00142982,0.0003991981,0.000535066,0.0002342763,0.0008779278,0.0006767313,0.0007225516,0.000958494],"category_scores_gemma":[0.009766429,0.0003370349,0.0003975853,0.0003840588,0.0003646304,0.001110287,0.0005735666,0.0009695908,0.0002904354],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005666667,"about_ca_system_score_gemma":0.0004718528,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003883903,"about_ca_topic_score_gemma":0.004299243,"domain_scores_codex":[0.9995348,0.0001443079,0.00004146843,0.0001159237,0.00008690471,0.00007663464],"domain_scores_gemma":[0.9976383,0.001697575,0.000179968,0.0002278076,0.0002227065,0.00003357475],"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":[0.0005154469,0.0002372252,0.019597,0.0002898012,0.0002360404,0.0002221606,0.0001656954,0.6819741,0.05325691,0.001049692,0.001229563,0.2412264],"study_design_scores_gemma":[0.00001647122,0.0002375058,0.007636873,0.00006115444,0.00005895226,0.00008710901,0.0000783037,0.9372371,0.05236743,0.001258677,0.0009304539,0.00002995281],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6719873,0.001758501,0.3195499,0.0005371162,0.0001098396,0.0001348462,0.0003293485,0.00247445,0.003118726],"genre_scores_gemma":[0.9581589,0.0002767571,0.04034503,0.00009308596,0.00001418007,0.00006842442,0.0003429489,0.0001143679,0.0005863656],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003883903,"threshold_uncertainty_score":0.008780837,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1388441580267909,"score_gpt":0.3209464661607065,"score_spread":0.1821023081339156,"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."}}