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A symbolic reasoning based anomaly detection for gas turbine subsystems

The detection of gas turbine engine anomalies is of great significance to its reliable economic operation. Considering the collective anomaly data to be detected sensitively, this paper presents a symbolic approach and applies it to anomaly detection of gas turbine subsystem. The trained finite state machine evaluates the posterior probabilities of observed symbol sequence. Thus, an anomaly detection strategy based on FSM estimation is used to detect the defects. Experimental results indicate that, despite the high performance of the model, the robustness of the model is strong, especially within a certain sequence length. Therefore, the proposed method can be a good way to promote the existing anomaly detection performance in gas turbine subsystem.