{"id":"W4293061168","doi":"10.1088/1361-6501/ac78c5","title":"Explainable 1DCNN with demodulated frequency features method for fault diagnosis of rolling bearing under time-varying speed conditions","year":2022,"lang":"en","type":"article","venue":"Measurement Science and Technology","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Demodulation; Fault (geology); Computer science; Vibration; Bearing (navigation); Autoencoder; Envelope (radar); Time–frequency analysis; Pattern recognition (psychology); SIGNAL (programming language); Stability (learning theory); Convolutional neural network; Artificial intelligence; Frequency band; Encoder; Control theory (sociology); Artificial neural network; Acoustics; Computer vision; Machine learning; Channel (broadcasting); Telecommunications; Physics; Filter (signal processing)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017106,0.0001676551,0.0002587078,0.0007830426,0.0005979075,0.00003331489,0.0004024137,0.00007257268,0.00003501427],"category_scores_gemma":[0.000291606,0.0001600245,0.00002817874,0.001526133,0.000227931,0.0001999293,0.0001283465,0.0002526637,5.672725e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003513827,"about_ca_system_score_gemma":0.00009658544,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007084834,"about_ca_topic_score_gemma":0.00002330237,"domain_scores_codex":[0.9982522,0.00002451855,0.0002321661,0.0003576089,0.0007352989,0.0003982716],"domain_scores_gemma":[0.9990708,0.00007638208,0.0000771886,0.0002818859,0.0004438485,0.00004994331],"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":[0.00001274334,0.0001334734,0.008671939,0.0001190027,0.0001031458,0.000006438199,0.0001681486,0.0601048,0.9148388,0.007642082,0.001418658,0.006780752],"study_design_scores_gemma":[0.0007514588,0.0004634673,0.001178348,0.0001072719,0.0000825111,0.00004393023,0.0004296448,0.0861739,0.8945566,0.01507671,0.0007267563,0.0004093828],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9108509,0.002119486,0.07744949,0.002621637,0.0001838177,0.002285774,0.00007955092,0.002170761,0.002238536],"genre_scores_gemma":[0.9663536,0.00002941427,0.03289634,0.00004308108,0.000007819734,0.0006317795,0.000005561595,0.00002447906,0.000007937872],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05550264,"threshold_uncertainty_score":0.6525611,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01927088401378763,"score_gpt":0.2787642421082465,"score_spread":0.2594933580944589,"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."}}