{"id":"W2599646198","doi":"10.1049/iet-smt.2016.0423","title":"Intelligent fault diagnosis approach with unsupervised feature learning by stacked denoising autoencoder","year":2017,"lang":"en","type":"article","venue":"IET Science Measurement & Technology","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":133,"is_retracted":false,"has_abstract":true,"ca_institutions":"BC Innovation Council; University of British Columbia","funders":"Core Research for Evolutional Science and Technology; Shandong Academy of Sciences; Canada Foundation for Innovation","keywords":"Autoencoder; Artificial intelligence; Computer science; Fault (geology); Artificial neural network; Noise reduction; Pattern recognition (psychology); Deep learning; Machine learning; Unsupervised learning; Feature (linguistics); Fault detection and isolation; Data mining","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001438784,0.0004494417,0.0004259595,0.0006161883,0.001033539,0.0004859899,0.002347022,0.0003059388,0.00002057781],"category_scores_gemma":[0.0007356326,0.0003831937,0.00006419139,0.00084343,0.001210262,0.0006582775,0.0003171552,0.0009782038,0.00001902585],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006631117,"about_ca_system_score_gemma":0.0001082137,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006656706,"about_ca_topic_score_gemma":0.00006817492,"domain_scores_codex":[0.996492,0.00003572123,0.0003136652,0.0008287359,0.001372885,0.0009569546],"domain_scores_gemma":[0.9978595,0.00002438269,0.0001678398,0.001369713,0.0004077471,0.0001708378],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00004859561,0.0009073333,0.3249628,0.0003961591,0.0003122237,0.00006407787,0.002083604,0.02439155,0.4462813,0.001444767,0.0338468,0.1652607],"study_design_scores_gemma":[0.000885258,0.0005377434,0.006818547,0.0004522818,0.00009505254,0.0000463676,0.0007415591,0.0788907,0.8605335,0.0006702136,0.04891326,0.001415551],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7512665,0.002771252,0.2146423,0.006851233,0.0006099489,0.002482297,0.00002040443,0.00964472,0.01171133],"genre_scores_gemma":[0.9799605,0.000288313,0.01904407,0.00005507244,0.00003051658,0.0005058785,0.000005097555,0.00006751125,0.00004304623],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4142521,"threshold_uncertainty_score":0.999862,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0223983592216372,"score_gpt":0.2681915661312421,"score_spread":0.2457932069096049,"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."}}