{"id":"W4391244162","doi":"10.1016/j.engappai.2024.107868","title":"An autoregressive model-based degradation trend prognosis considering health indicators with multiscale attention information","year":2024,"lang":"en","type":"article","venue":"Engineering Applications of Artificial Intelligence","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"China Scholarship Council; National Natural Science Foundation of China","keywords":"Autoregressive model; Computer science; Prognostics; Metric (unit); Nonlinear autoregressive exogenous model; Health indicator; Data mining; Degradation (telecommunications); Machine learning; Artificial intelligence; Econometrics; Artificial neural network; Health care; Mathematics","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":[0.0006149351,0.0005403787,0.0009899682,0.0005620198,0.00026322,0.0007999307,0.0007396283,0.0007388397,0.0009767946],"category_scores_gemma":[0.001733431,0.0002871638,0.0007175821,0.0006273979,0.0002170619,0.000900765,0.0004552686,0.0006690715,0.0003054654],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003000406,"about_ca_system_score_gemma":0.0008029186,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007192906,"about_ca_topic_score_gemma":0.005968513,"domain_scores_codex":[0.9997731,0.00003173115,0.00001745113,0.00008144406,0.00006255637,0.00003379343],"domain_scores_gemma":[0.9996647,0.0001196518,0.00004547837,0.00002696972,0.000126321,0.00001686892],"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":[0.000390317,0.0001605973,0.004564261,0.0002182035,0.0001703908,0.0002640596,0.0001228958,0.6669166,0.01850961,0.00620122,0.002432754,0.300049],"study_design_scores_gemma":[0.000002292502,0.00002543176,0.0003937601,0.000003398729,0.00001793126,0.00001589566,0.000003994234,0.9985684,0.0004794634,0.0003813325,0.0001036251,0.000004307662],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06027076,0.0006639158,0.936659,0.0002226196,0.0001297414,0.00003822231,0.0001372001,0.0005025275,0.00137588],"genre_scores_gemma":[0.9413806,0.0005254336,0.05499747,0.00008017389,0.00008927358,0.00006415624,0.0002625163,0.00003337179,0.002566831],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007192906,"threshold_uncertainty_score":0.01430207,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01240138616685221,"score_gpt":0.2931514707106116,"score_spread":0.2807500845437594,"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."}}