Application of bayesian forecasting to change detection and prognosis of gas turbine performance

Journal of Engineering for Gas Turbines and Power

Volumn 132, Issue 3, 2010, Pages

  Lipowsky, Holger ^a   Staudacher, Stephan ^a   Bauer, Michael ^b   Schmidt, Klaus Juergen ^b  

^a UNIVERSITY OF STUTTGART   (Germany)

^b MTU AERO ENGINES GMBH   (Germany)

Author keywords

Engine diagnostics; Failure detection; Gas turbine performance

Indexed keywords

ABRUPT CHANGE; BAYESIAN FORECASTING; CHANGE DETECTION; CONFIDENCE BOUNDS; CUMULATIVE DISTRIBUTION FUNCTION; CURRENT MODELS; DETECTION ALGORITHM; DETECTION RATES; DETECTION TECHNIQUE; DETERIORATION MECHANISM; DYNAMIC LINEAR MODEL; ENGINE DIAGNOSTICS; ENGINE PARAMETER; FAILURE DETECTION; FALSE DETECTIONS; FORECAST DISTRIBUTION; GAS TURBINE PERFORMANCE; HIGH DETECTION RATE; MATHEMATICAL TOOLS; MEAN VALUES; POTENTIAL CHANGE; POTENTIAL LIMITS; PROBABILITY DENSITIES; SINGLE EVENT; SINGLE FAULT; STANDARD DEVIATION; TEST PROGRAM; UNCERTAINTY LIMITS;

BAYESIAN NETWORKS; CASCADES (FLUID MECHANICS); DETERIORATION; DISTRIBUTION FUNCTIONS; EXPERT SYSTEMS; FORECASTING; FUZZY FILTERS; FUZZY LOGIC; FUZZY NEURAL NETWORKS; GAS TURBINES; SIGNAL DETECTION; TIME SERIES; TIME SERIES ANALYSIS; TURBOMACHINERY;

PROBABILITY DENSITY FUNCTION;

EID: 78149380976     PISSN: 07424795     EISSN: 15288919     Source Type: Journal    
DOI: 10.1115/1.3159367     Document Type: Article

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