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

作者：Li, Fei; Wang, Hongzhi^*; Zhou, Guowen; Yu, Daren; Li, Jiangzhong; Gao, Hong

来源：Energies, 2017, 10(5): 724.

DOI：10.3390/en10050724

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

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.

• 出版日期2017-5
• 单位哈尔滨工业大学