Preprint Article Version 1 This version not peer reviewed

Anomaly Detection on Gas Turbine Fuel System Using a Sequential Symbolic Method

Version 1 : Received: 13 April 2017 / Approved: 13 April 2017 / Online: 13 April 2017 (05:36:51 CEST)

A peer-reviewed article of this Preprint also exists.

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. 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.

Journal reference: Energies 2017, 10, 724
DOI: 10.3390/en10050724

Abstract

Anomaly detection plays a significant role in helping gas turbines run reliably and economically. Considering 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 to evaluate posterior probabilities of observing symbolic sequences and most probable state sequences they may locate. Hence an estimating based model and a decoding based model are used to identify anomalies in two different ways. Experimental results indicates that these two models have both ideal performance overall, and estimating based model has a strong ability in robustness, while decoding based model has a strong ability in accuracy, particularly in a certain range of length of sequence. Therefore, the proposed method can well facilitate existing symbolic dynamic analysis based anomaly detection methods especially in gas turbine domain.

Subject Areas

gas turbine fuel system; anomaly detection; symbolic dynamic analysis; time series

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