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

Fei Li, Hongzhi Wang, Guowen Zhou, Daren Yu, Jiangzhong Li and Hong Gao
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Fei Li: School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China
Hongzhi Wang: School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China
Guowen Zhou: School of Energy Science and Engineering, Harbin Institute of Technology, Harbin 150001, China
Daren Yu: School of Energy Science and Engineering, Harbin Institute of Technology, Harbin 150001, China
Jiangzhong Li: School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China
Hong Gao: School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China

Energies, 2017, vol. 10, issue 5, 1-22

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.

Keywords: gas turbine fuel system; anomaly detection; symbolic dynamic analysis; time series (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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