{"id":"W2437984376","doi":"10.1016/j.ymssp.2016.05.036","title":"Multipoint Optimal Minimum Entropy Deconvolution and Convolution Fix: Application to vibration fault detection","year":2016,"lang":"en","type":"article","venue":"Mechanical Systems and Signal Processing","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":433,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta; Microsoft (Canada)","funders":"","keywords":"Deconvolution; Blind deconvolution; Algorithm; Wiener deconvolution; Vibration; Impulse (physics); Mathematical optimization; Control theory (sociology); Impulse response; Filter (signal processing); Finite impulse response; Convolution (computer science); Iterative method; Entropy (arrow of time); Mathematics; Computer science; Acoustics; Artificial neural network; Artificial intelligence; Mathematical analysis","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003020069,0.0001745746,0.000198443,0.00009672184,0.0001340977,0.0001093989,0.00005656314,0.0001555795,0.000004153563],"category_scores_gemma":[0.00004057601,0.0001358466,0.00002402266,0.0001132896,0.00001954456,0.0003982221,0.00003285725,0.00008638536,0.000007720984],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001489707,"about_ca_system_score_gemma":0.000008284692,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007302273,"about_ca_topic_score_gemma":0.00002111902,"domain_scores_codex":[0.998913,0.00004817852,0.0003445875,0.0003147472,0.0001636905,0.000215778],"domain_scores_gemma":[0.9995546,0.00006407042,0.00007114234,0.00009428387,0.00007628233,0.0001395795],"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":[0.00002338742,0.00001311268,0.0000893433,0.0001411871,0.000006754746,4.444177e-7,0.0000492107,0.000632464,0.8053772,0.0004578441,0.00002856866,0.1931805],"study_design_scores_gemma":[0.0003706072,0.0001397361,0.0006981558,0.0002831894,0.00001669306,0.00002067384,0.00003586912,0.8479885,0.1491693,0.0002343071,0.0008152153,0.0002276958],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3014012,0.0003800648,0.6972887,0.00008658277,0.00005720631,0.0004546512,0.000004636267,0.0003152296,0.00001172289],"genre_scores_gemma":[0.9972728,0.00005171561,0.00213446,0.00002802851,0.0001577041,0.0003149966,0.000004298315,0.00002649976,0.0000094971],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8473561,"threshold_uncertainty_score":0.5539662,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006769148183000608,"score_gpt":0.2342843883299001,"score_spread":0.2275152401468995,"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."}}