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research-article

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

[+] Author and Article Information
Giuseppe Fabio Ceschini

Siemens AG, Nürnberg, Germany
giuseppe.ceschini@siemens.com

Nicolo' Gatta

Dipartimento di Ingegneria, Università degli Studi di Ferrara, Ferrara, Italy
ncl.gatta@gmail.com

Mauro Venturini

Dipartimento di Ingegneria, Università degli Studi di Ferrara, Ferrara, Italy
mauro.venturini@unife.it

Thomas Hubauer

Siemens AG, Nürnberg, Germany
thomas.hubauer@siemens.com

Alin Murarasu

Siemens AG, Nürnberg, Germany
alin.murarasu@siemens.com

1Corresponding author.

ASME doi:10.1115/1.4037963 History: Received July 14, 2017; Revised July 30, 2017

Abstract

Statistical parametric methodologies are widely employed in the analysis of time series of gas turbine sensor readings. Among parametric techniques, the k-s methodology demonstrates its effectiveness in the analysis of stationary time series. Furthermore, the simplicity and the clarity of this approach justify its direct application to industry. On the other hand, the k-s methodology usually proves to be unable to adapt to dynamic time series, since it identifies observations in a transient as outliers. As this limitation is caused by the nature of the methodology itself, two improved approaches are considered in this paper in addition to the standard k-s methodology. The two proposed methodologies maintain the same rejection rule of the standard k-s methodology, but differ in the portions of the time series from which statistical parameters (mean and standard deviation) are inferred. The first approach performs statistical inference by considering all observations prior to the current one, which are assumed reliable, plus a forward window containing a specified number of future observations. The second approach proposed in this paper is based on a moving window scheme. Simulated data are used to tune the parameters of the proposed improved methodologies and to prove their effectiveness in adapting to dynamic time series. The moving window approach is found to be the best on simulated data. Therefore, the performance of the moving window approach is further assessed towards both different simulated scenarios and field data taken on a gas turbine.

Copyright (c) 2017 by ASME
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