Aviation dynamotor fault diagnosis method based on FWA-RNN model
A fault diagnosis and generator technology, applied in the direction of instruments, adaptive control, control/regulation systems, etc., can solve the problems of low automation, low analysis efficiency, slow convergence speed, etc., to achieve fast and accurate diagnosis, speed up convergence speed, Effects that improve speed and accuracy
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[0023] The invention will be described in further detail below in conjunction with the accompanying drawings.
[0024] The present invention proposes a fault diagnosis method for aeronautical generators based on the FWA-RNN model. The method mainly includes fault mode and test signal analysis, signal acquisition, data preprocessing, using fireworks algorithm to optimize neural network initial parameters, and using recursive neural network Perform fault detection and performance detection. The process used is as figure 1 As shown, the specific operation includes the following steps:
[0025] First, determine the main fault types and numbers of aero-generators by consulting literature and experimental simulation. The main typical failure modes of aeronautical generators include internal short-circuit faults of stator windings, insulation faults of stator windings, inter-turn short-circuit faults of rotor windings, single-tube open-circuit faults, double-tube open-circuit fault...
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