A Bayesian machine learning method for sensor selection and fusion with application to on-board fault diagnostics

Mechanical Systems and Signal Processing

Volumn 24, Issue 1, 2010, Pages 182-192

  Subrahmanya, Niranjan ^a   Shin, Yung C ^a   Meckl, Peter H ^a  

^a PURDUE UNIVERSITY   (United States)

Author keywords

Condition monitoring; Diesel engine fault detection; Empirical Bayes; Feature group selection; Hierarchical Bayes; Sensor selection

Indexed keywords

BAYESIAN; BAYESIAN FRAMEWORKS; COMPLEX PROBLEMS; DATA RECORDS; EMPIRICAL BAYES; FAULT DIAGNOSTICS; FEATURE LEVEL; FEATURE SELECTION; FEATURE-GROUP SELECTION; GENERALIZED LINEAR MODEL; HIERARCHICAL BAYES; HIERARCHICAL BAYESIAN; MACHINE LEARNING METHODS; MAINTENANCE COST; MISCLASSIFICATION RATES; MODEL SELECTION; ONLINE DATABASE; OPTIMAL NUMBER; OPTIMAL SETS; OVERFITTING; RANK ORDER; SENSOR SELECTION; STATISTICAL MODELS; SYNTHETIC REGRESSION;

ALGORITHMS; BAYESIAN NETWORKS; CONDITION MONITORING; FAULT DETECTION; MAINTENANCE; PARAMETER ESTIMATION; SENSORS;

DIESEL ENGINES;

EID: 70349296776     PISSN: 08883270     EISSN: 10961216     Source Type: Journal    
DOI: 10.1016/j.ymssp.2009.06.010     Document Type: Article

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