基于混合算法的燃气轮机多传感器故障诊断

In order to solve the problem that it is difficult to realize gas turbine multi-sensor fault diagnosis based on Kalman filter, proposes a gas turbine multi-sensor fault diagnosis method based on a hybrid method. Firstly, based on the square root cubature Kalman filter (SRCKF) algorithm, a set of filters are constructed. The optimal state estimation of each filter is defined as a fault detection factor for feature extraction of sensor faults. Then, the density based clustering algorithm is proposed to cluster the fault detection factors to realize the detection and isolation of fault sensors. Finally, the maximum likelihood estimation (MLE) method is used to estimate the severity of the fault sensor. The proposed method is verified on a GT25000 three-axis gas turbine simulator. The simulation results show that the proposed method is effective, and the accuracy of multi-sensor fault diagnosis is higher than 95%.