In the extended multiple model adaptive estimation fault diagnosis algorithm, the extended Kalman filterhas theoretical limitations, and the establishment of accurate aircraft mathematical model is almost impossible. Meanwhile, there is no automatic method to optimally select the node number of deep neural network hiddenlayer. In this paper, a deep auto-encoder observer multiple-model fault diagnosis algorithm for aircraft actuatorfault is proposed. Based on the empirical formula of the basic auto-encoder hidden layer node number selection(three layered neural network), the recursive formula for deep auto-encoder hidden layer node number selectionare proposed. The deep auto-encoder observers for no-fault and different actuator faults are trained to observethe system state. Combined with multiple model adaptive estimation, the deep auto-encoder observer overcomesthe theoretical limitation of extended Kalman filter, and avoided the calculation of the nonlinear system Jacobianmatrix. The simulation results show that hidden layer node number selection recursive formula is useful. The faultdiagnosis algorithm is more efficient and has better performance compared to the standard methods.
In the extended multiple model adaptive estimation fault diagnosis algorithm, the extended Kalman filterhas theoretical limitations, and the establishment of accurate aircraft mathematical model is almost impossible. Meanwhile, there is no automatic method to optimally select the node number of deep neural network hiddenlayer. In this paper, a deep auto-encoder observer multiple-model fault diagnosis algorithm for aircraft actuatorfault is proposed. Based on the empirical formula of the basic auto-encoder hidden layer node number selection(three layered neural network), the recursive formula for deep auto-encoder hidden layer node number selectionare proposed. The deep auto-encoder observers for no-fault and different actuator faults are trained to observethe system state. Combined with multiple model adaptive estimation, the deep auto-encoder observer overcomesthe theoretical limitation of extended Kalman filter, and avoided the calculation of the nonlinear system Jacobianmatrix. The simulation results show that hidden layer node number selection recursive formula is useful. The faultdiagnosis algorithm is more efficient and has better performance compared to the standard methods.