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Research Papers: Gas Turbines: Oil and Gas Applications

A Comprehensive Approach for Detection, Classification, and Integrated Diagnostics of Gas Turbine Sensors

[+] Author and Article Information
Giuseppe Fabio Ceschini, Thomas Hubauer, Alin Murarasu

Siemens AG,
Nürnberg 90461, Germany

Nicolò Gatta, Mauro Venturini

Dipartimento di Ingegneria,
Università degli Studi di Ferrara,
Ferrara 44122, Italy

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 July 30, 2017; published online October 25, 2017. Editor: David Wisler.

J. Eng. Gas Turbines Power 140(3), 032402 (Oct 25, 2017) (9 pages) Paper No: GTP-17-1360; doi: 10.1115/1.4037964 History: Received July 14, 2017; Revised July 30, 2017

Anomaly detection in sensor time series is a crucial aspect for raw data cleaning in gas turbine (GT) industry. In addition to efficiency, a successful methodology for industrial applications should be also characterized by ease of implementation and operation. To this purpose, a comprehensive and straightforward approach for detection, classification, and integrated diagnostics of gas turbine sensors (named DCIDS) is proposed in this paper. The tool consists of two main algorithms, i.e., the anomaly detection algorithm (ADA) and the anomaly classification algorithm (ACA). The ADA identifies anomalies according to three different levels of filtering based on gross physics threshold application, intersensor statistical analysis (sensor voting), and single-sensor statistical analysis. Anomalies in the time series are identified by the ADA, together with their characteristics, which are analyzed by the ACA to perform their classification. Fault classes discriminate among anomalies according to their time correlation, magnitude, and number of sensors in which an anomaly is contemporarily identified. Results of anomaly identification and classification can subsequently be used for sensor diagnostic purposes. The performance of the tool is assessed in this paper by analyzing two temperature time series with redundant sensors taken on a Siemens GT in operation. The results show that the DCIDS is able to identify and classify different types of anomalies. In particular, in the first dataset, two severely incoherent sensors are identified and their anomalies are correctly classified. In the second dataset, the DCIDS tool proves to be capable of identifying and classifying clustered spikes of different magnitudes.

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Figures

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Fig. 1

DCIDS tool structure

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Fig. 4

Sensor voting procedure

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Fig. 5

AD and RD for the nondimensional temperature T1 dataset

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Fig. 6

Detection rules of statistical filter and noise removal filter

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Fig. 7

Anomaly classification algorithm

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Fig. 8

Nondimensional temperature T1 dataset

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Fig. 9

Nondimensional temperature T2 dataset

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Fig. 10

ADA application to the nondimensional temperature T1 dataset

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Fig. 11

Share of ADA results for the nondimensional temperature T1 dataset

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Fig. 12

ACA application to the nondimensional temperature T1 dataset—sensor voting (top) and statistical filter (bottom)

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Fig. 13

ADA application to the nondimensional temperature T2 dataset (k–σ methodology at third level)

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Fig. 14

ADA application to the nondimensional temperature T2 dataset (k-MAD methodology at third level)

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Fig. 15

Share of ADA results for the nondimensional temperature T2 dataset

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Fig. 16

ACA application to the nondimensional temperature T2 dataset—sensor voting (top) and statistical filter (bottom)

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