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research-article

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

[+] 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:10.1115/1.4037964 History: Received July 14, 2017; Revised July 30, 2017

Abstract

Anomaly detection in sensor time series is a crucial aspect for raw data cleaning in gas turbine industry. In fact, 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, inter-sensor 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 gas turbine in operation. The results show that the DICDS 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.

Copyright (c) 2017 by ASME
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