(Li J, Zhou D H, Si X S, et al. Review of incipient fault diagnosis methods[J]. Control Theory & Applications, 2012, 29(12): 1517-1529.)
[3]
Yuan L F, He Y G, Huang J Y, et al. A new neural-network-based fault diagnosis approach for analog circuits by using kurtosis and entropy as a preprocessor[J]. IEEE Trans on Instrumentation and Measurement, 2010, 59(3): 586-595.
[4]
Chen X Y, Zhou J Z, Xiao H, et al. Fault diagnosis based on comprehensive geometric characteristic and probability neural network[J]. Applied Mathematics and Computation, 2014, 230(1): 542-554.
[5]
Li X J, Yang G H. Fault diagnosis for non-linear single output systems based on adaptive high-gain observer[J]. IET Control Theory & Applications, 2013, 7(16): 1969-1977.
[6]
Shen Q, Jiang B, Shi P. Adaptive fault diagnosis for T-S fuzzy systems with sensor faults and system performance analysis[J]. IEEE Trans on Fuzzy Systems, 2014, 22(2): 274-285.
[7]
Ma H J, Yang G H. Detection and adaptive accommodation for actuator faults of a class of nonlinear systems[J]. IET Control Theory & Applications, 2012, 6(14): 2292-2307.
[8]
Zhang J, Swain A K, Nguang S K. Detection and isolation of incipient sensor faults for a class of uncertain non-linear systems[J]. IET Control Theory & Applications, 2012, 6(12): 1870-1880.
(Cao W, Cong W, Li J, et al. Fault diagnosis of discrete linear time-varying system based on iterative learning[J]. Control and Decision, 2013, 28(1): 137-141.)
(Yan B Y, Tian Z H, Shi S J, et al. Fault diagnosis for nonlinear time delay systems based on fault tracking approximator[J]. Control and Decision, 2009, 24(1): 133-136.)
[15]
Tan C P, Edwards C. Sliding mode observers for reconstruction of simultaneous actuator and sensor faults[C]. Proc of the 42nd IEEE Conf on Decision and Control. New York: IEEE Press, 2003: 1455-1460.
(Yan B Y. Research on several methods of fault diagnosis for nonlinear systems and their applications[D]. Shanghai: School of Electronic Information and Electric Engineering, Shanghai Jiaotong University, 2010.)
