In this paper, we have proposed diagnostic techniques using a
multilayered neural network where the weights in the network are
updated using node-decoupled extended Kalman filter (NDEKF)
training method. Sensor signals in both time domain and frequency
domain are analyzed to show the effectiveness of the NDEKF
algorithm in each domain. Comparisons of the NDEKF algorithm with
other popular neural network training algorithms such as extended
Kalman filter (EKF) and backpropagation (BP) will be discussed in
the paper through a system identification problem. First, the
simulation results reveal the comparison of outputs from actual
system and trained neural network. Secondly, the ability of
diagnosing a system with one normal condition and three known
fault conditions is demonstrated. Thirdly, the robustness of the
machine condition monitoring when the inputs to the system vary is
shown. The proposed technique works even when there is noise in
sensor signals as well.
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