{"id":"W4394755344","doi":"10.3390/s24082470","title":"Smart Sensor-Based Monitoring Technology for Machinery Fault Detection","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"","keywords":"Bearing (navigation); Fault (geology); Condition monitoring; Data acquisition; Engineering; SIGNAL (programming language); Hilbert–Huang transform; Fault detection and isolation; Signal processing; Rolling-element bearing; Vibration; Real-time computing; Computer science; Control engineering; Electronic engineering; Artificial intelligence; Actuator; White noise; Digital signal processing; Acoustics; Electrical engineering","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":[0.000131546,0.0002227266,0.0001835908,0.0005251367,0.0000637003,0.00006112846,0.0001179349,0.0002222086,0.00001154286],"category_scores_gemma":[0.00009543241,0.0002262196,0.0001146404,0.0004261622,0.00003014748,0.00006441196,0.0000161091,0.0003138132,0.00004861981],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001321564,"about_ca_system_score_gemma":0.0000101417,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002755304,"about_ca_topic_score_gemma":0.00001058438,"domain_scores_codex":[0.9990502,0.00001512457,0.0002184845,0.0002761935,0.0001177562,0.0003222453],"domain_scores_gemma":[0.9994537,0.0001730012,0.00001482911,0.0002680262,0.00003927997,0.00005121015],"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":[0.00003963202,0.00007125883,0.008371986,0.001805676,0.0002650624,0.0001355597,0.0002026093,0.1301976,0.448558,0.0007830191,0.004803015,0.4047666],"study_design_scores_gemma":[0.0001233876,0.00005430908,0.0002186263,0.0001149874,0.00002694874,0.00001479969,0.00003266814,0.4000672,0.5551075,0.0003985157,0.04359145,0.0002497085],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9546564,0.0007340602,0.03147759,0.0004062245,0.001603337,0.0005118874,0.00003801287,0.009585462,0.0009869961],"genre_scores_gemma":[0.9896868,0.00004772865,0.009464996,0.00001708535,0.0002996477,0.0002380697,0.000008547858,0.0001106209,0.0001264673],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4045169,"threshold_uncertainty_score":0.9224966,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009449119203829301,"score_gpt":0.2756914782754677,"score_spread":0.2662423590716385,"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."}}