|Deep Auto-encoder Observer Multiple-Model Fast Aircraft Actuator Fault Diagnosis Algorithm
Jun Ma, Shihong Ni, Wujie Xie, and Wenhan Dong*
International Journal of Control, Automation, and Systems, vol. 15, no. 4, pp.1641-1650, 2017
Abstract : "In the extended multiple model adaptive estimation fault diagnosis algorithm, the extended Kalman filter
has 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 hidden
layer. In this paper, a deep auto-encoder observer multiple-model fault diagnosis algorithm for aircraft actuator
fault 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 selection
are proposed. The deep auto-encoder observers for no-fault and different actuator faults are trained to observe
the system state. Combined with multiple model adaptive estimation, the deep auto-encoder observer overcomes
the theoretical limitation of extended Kalman filter, and avoided the calculation of the nonlinear system Jacobian
matrix. The simulation results show that hidden layer node number selection recursive formula is useful. The fault
diagnosis algorithm is more efficient and has better performance compared to the standard methods."
"Deep auto-encoder, fault detection and isolation, hidden layer node number, multiple model adaptive estimation."