{"id":"W2082302373","doi":"10.4236/jsip.2011.24046","title":"A Wavelet Spectrum Technique for Machinery Fault Diagnosis","year":2011,"lang":"en","type":"article","venue":"Journal of Signal and Information Processing","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University; Optech (Canada)","funders":"","keywords":"Wavelet; Computer science; Fault (geology); Rolling-element bearing; Demodulation; Pattern recognition (psychology); Fault detection and isolation; Bearing (navigation); Feature extraction; Wavelet transform; Vibration; SIGNAL (programming language); Artificial intelligence; Condition monitoring; Stationary wavelet transform; Feature (linguistics); Signal processing; Discrete wavelet transform; Engineering; Acoustics; Telecommunications; Computer hardware; Digital signal processing; Actuator","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003178559,0.0004615509,0.0003308961,0.0009594804,0.0001756573,0.000317004,0.0002754303,0.0004838232,0.001255708],"category_scores_gemma":[0.0007975416,0.0001472821,0.0003260464,0.001082491,0.0002785826,0.0005912867,0.000291606,0.0005378997,0.0006859194],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001241557,"about_ca_system_score_gemma":0.0002453119,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003090582,"about_ca_topic_score_gemma":0.0002712803,"domain_scores_codex":[0.9997948,0.00003757523,0.000012059,0.00002767701,0.000116375,0.00001156836],"domain_scores_gemma":[0.9997913,0.00007695227,0.00002620682,0.00002797839,0.00006852159,0.000008999905],"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":[0.0001162788,0.00004836832,0.0005044235,0.000239639,0.00003395379,0.0002138805,0.00006739245,0.0111231,0.2419929,0.01185016,0.001880929,0.7319291],"study_design_scores_gemma":[0.000062907,0.0005137741,0.005570406,0.00009937166,0.0001015371,0.002076628,0.0001153777,0.7163032,0.205498,0.01760589,0.05198818,0.00006469242],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008752112,0.0008256587,0.9888559,0.00008104727,0.00007825096,0.00002961753,0.00004052373,0.000240381,0.001096516],"genre_scores_gemma":[0.2182484,0.00271289,0.7750485,0.0001043736,0.0001789942,0.00008814905,0.0002732998,0.00006845285,0.003276965],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001255708,"threshold_uncertainty_score":0.004200816,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01173760744484803,"score_gpt":0.2363748439542621,"score_spread":0.2246372365094141,"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."}}