The reliability of gas turbine (GT) health state monitoring and forecasting depends on the quality of sensor measurements directly taken from the unit. Outlier detection techniques have acquired a major importance, as they are capable of removing anomalous measurements and improve data quality. To this purpose, statistical parametric methodologies are widely employed thanks to the limited knowledge of the specific unit required to perform the analysis. The backward and forward moving window (BFMW) k–σ methodology proved its effectiveness in a previous study performed by the authors, to also manage dynamic time series, i.e., during a transient. However, the estimators used by the k–σ methodology are usually characterized by low statistical robustness and resistance. This paper aims at evaluating the benefits of implementing robust statistical estimators for the BFMW framework. Three different approaches are considered in this paper. The first methodology, k-MAD, replaces mean and standard deviation (SD) of the k–σ methodology with median and mean absolute deviation (MAD), respectively. The second methodology, σ-MAD, is a novel hybrid scheme combining the k–σ and the k-MAD methodologies for the backward and the forward windows, respectively. Finally, the biweight methodology implements biweight mean and biweight SD as location and dispersion estimators. First, the parameters of these methodologies are tuned and the respective performance is compared by means of simulated data. Different scenarios are considered to evaluate statistical efficiency, robustness, and resistance. Subsequently, the performance of these methodologies is further investigated by injecting outliers in field datasets taken on selected Siemens GTs. Results prove that all the investigated methodologies are suitable for outlier identification. Advantages and drawbacks of each methodology allow the identification of different scenarios in which their application can be most effective.
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Research-Article
Resistant Statistical Methodologies for Anomaly Detection in Gas Turbine Dynamic Time Series: Development and Field Validation
Giuseppe Fabio Ceschini,
Giuseppe Fabio Ceschini
Siemens AG,
Nürnberg 90461, Germany
Nürnberg 90461, Germany
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Nicolò Gatta,
Nicolò Gatta
Dipartimento di Ingegneria,
Università degli Studi di Ferrara,
Ferrara 44122, Italy
Università degli Studi di Ferrara,
Ferrara 44122, Italy
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Mauro Venturini,
Mauro Venturini
Dipartimento di Ingegneria,
Università degli Studi di Ferrara,
Ferrara 44122, Italy
Università degli Studi di Ferrara,
Ferrara 44122, Italy
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Thomas Hubauer,
Thomas Hubauer
Siemens AG,
Nürnberg 90461, Germany
Nürnberg 90461, Germany
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Alin Murarasu
Alin Murarasu
Siemens AG,
Nürnberg 90461, Germany
Nürnberg 90461, Germany
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Giuseppe Fabio Ceschini
Siemens AG,
Nürnberg 90461, Germany
Nürnberg 90461, Germany
Nicolò Gatta
Dipartimento di Ingegneria,
Università degli Studi di Ferrara,
Ferrara 44122, Italy
Università degli Studi di Ferrara,
Ferrara 44122, Italy
Mauro Venturini
Dipartimento di Ingegneria,
Università degli Studi di Ferrara,
Ferrara 44122, Italy
Università degli Studi di Ferrara,
Ferrara 44122, Italy
Thomas Hubauer
Siemens AG,
Nürnberg 90461, Germany
Nürnberg 90461, Germany
Alin Murarasu
Siemens AG,
Nürnberg 90461, Germany
Nürnberg 90461, Germany
Contributed by the Oil and Gas Applications Committee of ASME for publication in the JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER. Manuscript received July 14, 2017; final manuscript received August 10, 2017; published online November 21, 2017. Editor: David Wisler.
J. Eng. Gas Turbines Power. May 2018, 140(5): 052401 (11 pages)
Published Online: November 21, 2017
Article history
Received:
July 14, 2017
Revised:
August 10, 2017
Citation
Fabio Ceschini, G., Gatta, N., Venturini, M., Hubauer, T., and Murarasu, A. (November 21, 2017). "Resistant Statistical Methodologies for Anomaly Detection in Gas Turbine Dynamic Time Series: Development and Field Validation." ASME. J. Eng. Gas Turbines Power. May 2018; 140(5): 052401. https://doi.org/10.1115/1.4038155
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