Optimization of Statistical Methodologies for Anomaly Detection in Gas Turbine Dynamic Time Series

Giuseppe Fabio Ceschini
Giuseppe Fabio Ceschini
Nicolò Gatta
Nicolò Gatta
Alin Murarasu
Alin Murarasu

JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER-TRANSACTIONS OF THE ASME, pp. 0324012018.

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Abstract:

Statistical parametric methodologies are widely employed in the analysis of time series of gas turbine (GT) sensor readings. These methodologies identify outliers as a consequence of excessive deviation from a statistical-based model, derived from available observations. Among parametric techniques, the k-sigma methodology demonstrates it...More

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