Li, F.; Wang, H.; Zhou, G.; Yu, D.; Li, J.; Gao, H. Anomaly Detection in Gas Turbine Fuel Systems Using a Sequential Symbolic Method. Energies2017, 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.
Li, F.; Wang, H.; Zhou, G.; Yu, D.; Li, J.; Gao, H. Anomaly Detection in Gas Turbine Fuel Systems Using a Sequential Symbolic Method. Energies2017, 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.
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.
Keywords
gas turbine fuel system; anomaly detection; symbolic dynamic analysis; time series
Subject
Engineering, Mechanical Engineering
Copyright:
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.
