{"id":"W3126618193","doi":"10.1109/tmech.2021.3058061","title":"Modified Stacked Autoencoder Using Adaptive Morlet Wavelet for Intelligent Fault Diagnosis of Rotating Machinery","year":2021,"lang":"en","type":"article","venue":"IEEE/ASME Transactions on Mechatronics","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":216,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Horizon 2020; National Natural Science Foundation of China","keywords":"Morlet wavelet; Autoencoder; Fault (geology); Wavelet; Artificial intelligence; Computer science; Pattern recognition (psychology); Engineering; Wavelet transform; Deep learning; Discrete wavelet transform; Geology; Seismology","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002910122,0.0004384673,0.0005708883,0.0002942595,0.0001706692,0.00004630415,0.0002628984,0.0002418776,0.0001398293],"category_scores_gemma":[0.00004620379,0.0004894953,0.0004004849,0.0004518572,0.00004539586,0.0002206616,0.000007544305,0.0005685039,0.000005996773],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003645358,"about_ca_system_score_gemma":0.0001281399,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009844236,"about_ca_topic_score_gemma":0.0001722926,"domain_scores_codex":[0.9977793,0.00009462297,0.0007313049,0.0004723826,0.0003746301,0.0005477786],"domain_scores_gemma":[0.9984369,0.0004908014,0.0001323639,0.0005549946,0.0002514558,0.0001334546],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000415076,0.0003223422,0.00001668237,0.0002165586,0.0002691094,0.000006505547,0.0004075702,0.9629362,0.005221984,0.001097288,0.0002257201,0.02923857],"study_design_scores_gemma":[0.0004288342,0.0001363497,0.000004598808,0.0001505471,0.0001299003,0.000006580072,0.0001694324,0.6971916,0.3002553,0.0007837591,0.0004101804,0.0003329113],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1165333,0.0003123652,0.8805035,0.00009041992,0.0005201579,0.0007335563,0.0006461633,0.0005440536,0.0001164819],"genre_scores_gemma":[0.7718711,0.0005321138,0.2266351,0.00007014548,0.00005495772,0.0006109104,0.0000435963,0.0001375519,0.00004444753],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6553379,"threshold_uncertainty_score":0.9997557,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03604493688424423,"score_gpt":0.2974490621712526,"score_spread":0.2614041252870084,"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."}}