{"id":"W2604493927","doi":"10.1109/tia.2017.2691736","title":"Online Unbalanced Rotor Fault Detection of an IM Drive Based on Both Time and Frequency Domain Analyses","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Industry Applications","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":83,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Fault (geology); Induction motor; Fast Fourier transform; Fault detection and isolation; Rotor (electric); Condition monitoring; Frequency domain; Signal processing; Vibration; Wavelet; Computer science; Discrete wavelet transform; SIGNAL (programming language); Wavelet transform; Control theory (sociology); Engineering; Electronic engineering; Digital signal processing; Artificial intelligence; Acoustics; Algorithm; Electrical engineering; Computer vision; Actuator; Physics; Voltage","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001348898,0.0003091764,0.0002820437,0.0007117823,0.0001020591,0.0002322759,0.0001603871,0.0002916029,0.0006841391],"category_scores_gemma":[0.0006076047,0.00009700452,0.0001291618,0.000273877,0.0001306081,0.0004062668,0.0001559006,0.0001689305,0.000225626],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009167792,"about_ca_system_score_gemma":0.00008692052,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003359274,"about_ca_topic_score_gemma":0.0004456976,"domain_scores_codex":[0.9998729,0.00001789088,0.000008023962,0.00002492621,0.00006542459,0.0000108561],"domain_scores_gemma":[0.9997844,0.00007430027,0.00004469619,0.00001906037,0.00006652901,0.0000111016],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005488351,0.0001619813,0.007456388,0.0001613648,0.00002987511,0.0003664895,0.000159053,0.01136977,0.4742569,0.0009010243,0.0007531701,0.5038353],"study_design_scores_gemma":[0.00004152821,0.0007008975,0.06747533,0.00003772787,0.00006396243,0.001426346,0.0001750772,0.7035359,0.2219626,0.001206322,0.003323377,0.00005083012],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4504398,0.000295197,0.5451329,0.0001024967,0.00005094608,0.00005157199,0.0001089911,0.0008902219,0.002927806],"genre_scores_gemma":[0.9274189,0.0001442508,0.07127563,0.00002225548,0.00001845219,0.00002379601,0.00008774647,0.00002585697,0.0009830836],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0007117823,"threshold_uncertainty_score":0.002288699,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01917924787903764,"score_gpt":0.3267179053393546,"score_spread":0.3075386574603169,"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."}}