Anomaly Detection in Gas Turbine Fuel Systems Using a Sequential Symbolic Method
AbstractAnomaly 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
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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(5):724.Chicago/Turabian Style
Li, Fei; Wang, Hongzhi; Zhou, Guowen; Yu, Daren; Li, Jiangzhong; Gao, Hong. 2017. "Anomaly Detection in Gas Turbine Fuel Systems Using a Sequential Symbolic Method." Energies 10, no. 5: 724.
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