{"id":"W3205404431","doi":"10.3390/signals2040040","title":"Bearing Prognostics: An Instance-Based Learning Approach with Feature Engineering, Data Augmentation, and Similarity Evaluation","year":2021,"lang":"en","type":"article","venue":"Signals","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Prognostics; Feature (linguistics); Computer science; Similarity (geometry); Aggregate (composite); Artificial intelligence; Pattern recognition (psychology); Data mining; Bearing (navigation); Feature engineering; Component (thermodynamics); Spectrogram; Principal component analysis; Machine learning; Range (aeronautics); Test data; Engineering; Deep learning","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005445214,0.0001743886,0.0001648703,0.00007484916,0.00006893164,0.0001304103,0.0001602786,0.00008611629,0.00003370268],"category_scores_gemma":[0.0002443931,0.0001753664,0.00001241567,0.0002237371,0.00001716611,0.0003809212,0.00005343107,0.0003055349,7.67683e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005397889,"about_ca_system_score_gemma":0.00006114414,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000960456,"about_ca_topic_score_gemma":0.00002655628,"domain_scores_codex":[0.99892,0.00007094075,0.0001436619,0.0003494566,0.0003306069,0.0001853716],"domain_scores_gemma":[0.9992709,0.00007561145,0.00003597894,0.0003955882,0.00014517,0.00007680251],"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":[0.0000129825,0.0001211703,0.0303313,0.0004599904,0.00009824549,0.00001644561,0.0004005383,0.9375198,0.0149253,0.0001140764,0.001637826,0.01436232],"study_design_scores_gemma":[0.0004453562,0.00005708041,0.008080438,0.000122488,0.00007681701,0.000007405581,0.00005463683,0.9628745,0.02646324,0.0000335736,0.001534145,0.0002503513],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6140774,0.002764013,0.378095,0.000176521,0.00008818475,0.001258852,0.000103611,0.001817063,0.001619324],"genre_scores_gemma":[0.894591,0.00004021281,0.1038034,0.00004379026,0.00005894661,0.0001124955,0.001295302,0.00004599029,0.000008888178],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2805136,"threshold_uncertainty_score":0.7151236,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03255508353921237,"score_gpt":0.2953239355981037,"score_spread":0.2627688520588913,"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."}}