Detection of process anomalies using an improved statistical learning framework

Expert Systems with Applications

Volumn 38, Issue 3, 2011, Pages 1356-1363

  An, Sang Ha ^a   Heo, Gyunyoung ^b   Chang, Soon Heung ^a  

^a Korea Advanced Institute of Science and Technology (KAIST)   (South Korea)

^b Kyung Hee University   (South Korea)

Author keywords

Condition based maintenance; Kernel regression; Process anomalies; Sensor anomalies; Statistical learning

Indexed keywords

CONDITION BASED MAINTENANCE; CONFIGURATION FLEXIBILITY; CONTEXT-FREE; CORRELATED VARIABLES; EMPIRICAL MODEL; HEALTH CONDITION; HISTORICAL OBSERVATION; KERNEL REGRESSION; MAINTENANCE TECHNOLOGIES; NOVEL STRATEGIES; NUMBER OF METHODS; PHYSICAL MODEL; PHYSICAL PRINCIPLES; PILOT SCALE; PROCESS ANOMALIES; REAL-TIME DIAGNOSIS; RESIDUAL LIFETIME; STATISTICAL CORRELATION; STATISTICAL LEARNING; WORK SITES;

DIAGNOSIS; DIELECTRIC PROPERTIES; GROUP DELAY; MAINTENANCE; SENSORS; STATISTICS;

MODELS;

EID: 78049530724     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2010.07.031     Document Type: Article

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