{"id":"W4283218269","doi":"10.1109/iemtronics55184.2022.9795726","title":"Predictive Maintenance and Condition Monitoring in Machine Tools: An IoT Approach","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS)","topic":"Advanced machining processes and optimization","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; McMaster University","funders":"","keywords":"Downtime; Predictive maintenance; Internet of Things; Computer science; Condition monitoring; Quality (philosophy); Reliability engineering; Preventive maintenance; Selection (genetic algorithm); Embedded system; Engineering; Machine learning","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":[0.0004185813,0.0003226885,0.000300232,0.0002493752,0.0002596538,0.0001575712,0.000378294,0.0001022243,0.00007804715],"category_scores_gemma":[0.00002863145,0.0003773765,0.00004689342,0.0002267667,0.00005256674,0.0004179108,0.0001635123,0.001034061,0.000001123934],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008055731,"about_ca_system_score_gemma":0.00017437,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003101262,"about_ca_topic_score_gemma":0.00007743175,"domain_scores_codex":[0.9979448,0.00006272625,0.000401443,0.0005809095,0.0004122515,0.0005978149],"domain_scores_gemma":[0.9993642,0.00006093134,0.0001314052,0.0002204095,0.0001027671,0.000120268],"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":[0.0001397031,0.0001367377,0.0005176467,0.00006241355,0.0001120214,0.000006854929,0.0007532687,0.8565785,0.003194011,0.1124856,0.00003213505,0.02598112],"study_design_scores_gemma":[0.001116408,0.0003582444,0.0001888752,0.00003024695,0.00002554139,0.00004584189,0.0008732558,0.9804391,0.000641115,0.008549638,0.00732005,0.0004116963],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6168717,0.01573388,0.3601112,0.0003874954,0.001962629,0.001212002,0.0005994224,0.0004904704,0.002631174],"genre_scores_gemma":[0.9888145,0.007035099,0.003042236,0.00004729884,0.0001098371,0.0003688507,0.0003466844,0.00006522574,0.0001702299],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3719428,"threshold_uncertainty_score":0.9998678,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01587660657889465,"score_gpt":0.2537900848892484,"score_spread":0.2379134783103537,"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."}}