Intelligent analytical redundancy method of control system sensors based on APU

For the sensors fault of auxiliary power unit (APU) control system, an intelligent analytical redundancy method of sensors was proposed based on online sequence extreme learning machine by improved covariance optimization algorithm. The covariance online sequence extreme learning machine (COSELM) algorithm method could adaptively update the weight coefficient of a single online sequence extreme learning machine according to the minimum variance of prediction error, exploit and weigh the advantages of each learning model. While increasing the stability and generalization of the model, the COSELM algorithm could improve the intelligent analytical redundancy accuracy of sensors. The simulation experiments using the sensors data of APU were carried out. The results indicated that the proposed COSELM method can solve the reconstruction problem when the sensors encountered the bias fault and the reconstruction error was less than 1%. Consequently, it is suitable for different engines to provide reliable analytical redundancy. © 2021, Editorial Department of Journal of Aerospace Power. All right reserved.