{"id":"W2129797471","doi":"","title":"CONDITION BASED MAINTENANCE USING PROPORTIONAL HAZARDS MODEL","year":2009,"lang":"en","type":"dissertation","venue":"Spectrum Research Repository (Concordia University)","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Condition-based maintenance; Condition monitoring; Preventive maintenance; Engineering; Predictive maintenance; Reliability engineering; Maintenance actions; Reliability (semiconductor); Optimal maintenance; Multi-objective optimization; Computer science; Machine learning","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001265835,0.0009220844,0.0009214964,0.0008509686,0.0003739423,0.001051574,0.002346264,0.001125698,0.005348068],"category_scores_gemma":[0.003111224,0.0003864163,0.0009475767,0.0009259384,0.000578485,0.001215144,0.000722374,0.001132395,0.0005063793],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001019507,"about_ca_system_score_gemma":0.001061943,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009734417,"about_ca_topic_score_gemma":0.005558948,"domain_scores_codex":[0.9992694,0.0001778955,0.00002949364,0.0001940312,0.0002239565,0.0001051629],"domain_scores_gemma":[0.9988315,0.0007471885,0.0001438737,0.00006892392,0.0001727054,0.00003594928],"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":[0.0000754853,0.00007750115,0.002136611,0.00008373329,0.00005419603,0.0001298019,0.00006101949,0.946631,0.001005758,0.0206123,0.001389905,0.02774268],"study_design_scores_gemma":[0.000008468681,0.00002003376,0.0002862991,0.000003071573,0.00001454028,0.00002344599,0.000004790188,0.9953688,0.00009257086,0.003770988,0.0004021889,0.000004763674],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03784651,0.0006908722,0.9492617,0.0005813288,0.0001323087,0.0001824379,0.0004857341,0.0005507625,0.01026842],"genre_scores_gemma":[0.9205236,0.0008308737,0.06020543,0.0001583098,0.0001150684,0.0003484827,0.0004591142,0.00007955352,0.01727947],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009734417,"threshold_uncertainty_score":0.01935554,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02101120618051855,"score_gpt":0.3082438113698314,"score_spread":0.2872326051893129,"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."}}