{"id":"W3138990266","doi":"10.36001/ijphm.2017.v8i1.2532","title":"Condition Based Maintenance of Low Speed Rolling Element Bearings using Hidden Markov Model","year":2020,"lang":"en","type":"article","venue":"International Journal of Prognostics and Health Management","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Hidden Markov model; Bearing (navigation); Residual; Vibration; Fault (geology); Condition-based maintenance; Reliability engineering; Condition monitoring; Failure rate; Engineering; Computer science; Hidden semi-Markov model; Markov chain; Markov model; Pattern recognition (psychology); Artificial intelligence; Machine learning; Algorithm; Markov property","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":[0.0004814484,0.0004204325,0.0006598426,0.0004365039,0.0002132248,0.0004488399,0.0006604182,0.0004125229,0.001055941],"category_scores_gemma":[0.001526378,0.0003161643,0.0005296544,0.000229106,0.0002501128,0.0005605408,0.0002634799,0.0005273786,0.0002172961],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006172871,"about_ca_system_score_gemma":0.0006309436,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01213263,"about_ca_topic_score_gemma":0.01224872,"domain_scores_codex":[0.999783,0.00003715917,0.00001439755,0.00006662797,0.00006759406,0.00003121474],"domain_scores_gemma":[0.9993201,0.0004437872,0.0000857166,0.0000341355,0.00009401163,0.00002213977],"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":[0.0001385306,0.00004561615,0.00339066,0.00005232323,0.00003656256,0.00007678272,0.00005297979,0.9543179,0.0041957,0.001668596,0.0002841295,0.03574022],"study_design_scores_gemma":[0.000002468882,0.00001546518,0.0005376323,0.000001871801,0.000006239397,0.000006900011,0.000002043672,0.998519,0.0004068581,0.0004491944,0.00004926564,0.000003002084],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1448554,0.0004288106,0.8518494,0.0001315895,0.0000417476,0.00004194259,0.0002141098,0.00112274,0.001314265],"genre_scores_gemma":[0.9728017,0.0001356525,0.02583846,0.0000162402,0.00001471412,0.00002774182,0.0002068974,0.00002325985,0.0009352597],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01213263,"threshold_uncertainty_score":0.02412397,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02755443777433166,"score_gpt":0.2803215384581931,"score_spread":0.2527671006838615,"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."}}