Accurate prediction of thermoacoustic instability is a prerequisite for thermoacoustic control to avoid the damage of combustion chamber, however, this problem has not been completely solved yet. This paper proposes a data-driven method based on the Elman neural network (ENN) to predict the value of acoustic pressure of combustion instability. As a comparison, a model based on support vector machine (SVM) was built. It is proved that ENN has better prediction performance with a certain predicted time horizon compared to the SVM method. What is more, the prediction model based on ENN can adapt to time-varying characteristics of the transition scenario which is characterized by amplitude modulation, multiple frequencies, and irregular bursts. ENN model still maintains enough prediction accuracy for various input training sets, indicating that ENN can fully mine the features of data and has a strong feature extraction ability in combustion oscillation prediction. Hence, it is demonstrated that ENN is a promising prediction tool for thermoacoustic instability under various combustion conditions. These findings are of great significance for the accurate prediction and control of thermoacoustic instability.

Based on the Kalman filter theory, detecting sensor faults in gas turbine control system and performing output reconstruction for fault signals is a hot focus in recent years. The selection of detection thresholds directly affects the rate and accuracy of fault diagnosis, which is extremely important for the safe operating of control system and its value is usually determined by actual experience and the noise characteristics of the sensor itself. Based on characteristics of the sensor and the observed signals, the cost function of the threshold is constructed by the combination of the leakage alarm probability and the false alarm probability, which is then used as the fitness function in the particle swarm optimization (PSO) algorithm, and the inertia weight in the iterative search process is adaptively changed. So, an improved dynamic weight adaptive threshold determination method based on PSO algorithm comes into being. The simulation comparison of the proposed method with the traditional threshold determination method shows that the timeliness, accuracy and integrity of the method is improved somewhat.