{"id":"W4400951916","doi":"10.1016/j.ymssp.2024.111751","title":"Normalizing vibration signals with a novel piecewise power fitting method for intelligent fault detection of rotating machinery","year":2024,"lang":"en","type":"article","venue":"Mechanical Systems and Signal Processing","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Anhui University; Natural Sciences and Engineering Research Council of Canada; Natural Science Foundation of Anhui Province; Tsinghua University; National Natural Science Foundation of China; University of Alberta","keywords":"Piecewise; Vibration; Fault (geology); Control theory (sociology); Power (physics); Fault detection and isolation; Computer science; Engineering; Acoustics; Electronic engineering; Mathematics; Electrical engineering; Physics; Artificial intelligence; Mathematical analysis; Actuator","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":[0.001036645,0.0002327618,0.0003515618,0.0001591958,0.000123149,0.0002654865,0.00008287947,0.0001281642,0.000004827275],"category_scores_gemma":[0.00005721707,0.0001850741,0.00006835583,0.0002698147,0.00001273222,0.0003515984,0.00002859044,0.00022649,3.271658e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004877867,"about_ca_system_score_gemma":0.00002259966,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009158525,"about_ca_topic_score_gemma":0.00001284805,"domain_scores_codex":[0.9985718,0.00004682179,0.0005814965,0.0003154884,0.0002337696,0.000250619],"domain_scores_gemma":[0.9992653,0.0003596197,0.0001110659,0.00008204283,0.000106895,0.00007513969],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000306725,0.00002667868,0.00001994894,0.003579953,0.00006406269,0.000003116717,0.0005167811,0.03051708,0.766332,0.0007944197,0.000009746957,0.1981055],"study_design_scores_gemma":[0.0001290478,0.000183193,0.00000482211,0.001752629,0.000043886,0.00003621041,0.0001890262,0.8105858,0.1865057,0.0001600328,0.0002170891,0.0001925128],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03200186,0.002738676,0.9639575,0.00001835912,0.0000987118,0.0005902646,0.00001767495,0.0004967466,0.00008016224],"genre_scores_gemma":[0.9338105,0.00001044943,0.06575737,0.00001525274,0.0001079002,0.0002236123,0.000005812602,0.00006324738,0.000005897482],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9018086,"threshold_uncertainty_score":0.7547104,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01584813914936846,"score_gpt":0.2914862072846062,"score_spread":0.2756380681352377,"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."}}