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Energies 2017, 10(5), 724; doi:10.3390/en10050724

Anomaly Detection in Gas Turbine Fuel Systems Using a Sequential Symbolic Method

1
School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China
2
School of Energy Science and Engineering, Harbin Institute of Technology, Harbin 150001, China
*
Author to whom correspondence should be addressed.
Academic Editor: Antonio Calvo Hernández
Received: 12 April 2017 / Revised: 12 May 2017 / Accepted: 14 May 2017 / Published: 20 May 2017
(This article belongs to the Section Energy Fundamentals and Conversion)

Abstract

Anomaly detection plays a significant role in helping gas turbines run reliably and economically. Considering the collective anomalous data and both sensitivity and robustness of the anomaly detection model, a sequential symbolic anomaly detection method is proposed and applied to the gas turbine fuel system. A structural Finite State Machine is used to evaluate posterior probabilities of observing symbolic sequences and the most probable state sequences they may locate. Hence an estimation-based model and a decoding-based model are used to identify anomalies in two different ways. Experimental results indicate that both models have both ideal performance overall, but the estimation-based model has a strong robustness ability, whereas the decoding-based model has a strong accuracy ability, particularly in a certain range of sequence lengths. Therefore, the proposed method can facilitate well existing symbolic dynamic analysis- based anomaly detection methods, especially in the gas turbine domain. View Full-Text
Keywords: gas turbine fuel system; anomaly detection; symbolic dynamic analysis; time series gas turbine fuel system; anomaly detection; symbolic dynamic analysis; time series
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Li, F.; Wang, H.; Zhou, G.; Yu, D.; Li, J.; Gao, H. Anomaly Detection in Gas Turbine Fuel Systems Using a Sequential Symbolic Method. Energies 2017, 10, 724.

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