{"id":"W4392652657","doi":"10.1016/j.measurement.2024.114451","title":"Extracting random forest features with improved adaptive particle swarm optimization for industrial robot fault diagnosis","year":2024,"lang":"en","type":"article","venue":"Measurement","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":57,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"National Key Research and Development Program of China; Natural Science Foundation of Chongqing; National Natural Science Foundation of China","keywords":"Particle swarm optimization; Random forest; Fault (geology); Robot; Computer science; Feature (linguistics); Artificial intelligence; Wavelet; Feature extraction; Set (abstract data type); Pattern recognition (psychology); Data mining; Algorithm","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":[0.0004469325,0.0001580859,0.0001733219,0.00004830782,0.00008860629,0.0001586468,0.00005694958,0.00008418942,0.00001460856],"category_scores_gemma":[0.00009330187,0.0001258737,0.00007476134,0.0001526854,0.000009528545,0.0001463469,0.000005151422,0.0001506311,0.000004910883],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002180276,"about_ca_system_score_gemma":0.00003178962,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007328062,"about_ca_topic_score_gemma":0.0004146706,"domain_scores_codex":[0.9990313,0.00003220324,0.000218415,0.0001959813,0.0002876991,0.0002343668],"domain_scores_gemma":[0.9996071,0.00008395933,0.00002957778,0.0001094277,0.0001014548,0.00006851226],"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":[0.00036257,0.00002429661,0.0001157246,0.00006108266,0.0002258098,0.000002623084,0.0002297762,0.9680331,0.005520866,0.00002784743,0.0009583118,0.02443803],"study_design_scores_gemma":[0.003296515,0.0001963619,0.00006971332,0.0001858888,0.00009569235,0.000004431537,0.0002666324,0.9640533,0.02672555,0.000005508297,0.00491027,0.0001900851],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04008386,0.003267967,0.949223,0.0004438879,0.002613703,0.002683265,0.00002030977,0.001103741,0.0005602706],"genre_scores_gemma":[0.9973261,0.00001356709,0.0007936886,0.0000155184,0.000456161,0.001288697,0.000004271463,0.00004335546,0.00005863331],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9572423,"threshold_uncertainty_score":0.513298,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04222036006455371,"score_gpt":0.2295403305296606,"score_spread":0.1873199704651068,"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."}}