{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000396004,0.0005493721,0.000349954,0.001205043,0.0003182255,0.001398869,0.0009236953,0.001060994,0.001253874],"category_scores_gemma":[0.0006944149,0.0002573622,0.0004006312,0.0008282863,0.0007313036,0.002047848,0.000575283,0.0006843654,0.0003104944],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006182157,"about_ca_system_score_gemma":0.0003551915,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008152202,"about_ca_topic_score_gemma":0.0008952824,"domain_scores_codex":[0.9996005,0.00005889711,0.00001429486,0.00006280984,0.0002378838,0.00002565967],"domain_scores_gemma":[0.9996227,0.0001414396,0.00005937339,0.00005708859,0.0001028654,0.00001655824],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000233066,0.0003821857,0.005456936,0.001087372,0.0001701969,0.0007869349,0.0005092921,0.1095221,0.08128923,0.1592591,0.007155444,0.6341481],"study_design_scores_gemma":[0.00003434606,0.0006103477,0.008000243,0.0005154771,0.0001713458,0.001277441,0.0005822687,0.7485462,0.04017427,0.1198675,0.08008229,0.0001383307],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02085326,0.01793198,0.9204496,0.002439216,0.0004820446,0.0001688428,0.0001098281,0.0007727756,0.03679246],"genre_scores_gemma":[0.6929953,0.01650885,0.2715224,0.0007688845,0.0007244103,0.0001848676,0.0001471024,0.00007852799,0.01706962],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001398869,"threshold_uncertainty_score":0.004485488,"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."}}