Adaptive Wavelet Packet Neural Network Based Fault Diagnosis for Missile’s Amplifier

Abstract

Amplifier is a very important device in the missile control system and its working state directly decides the missile’s flying stability and hitting precision. In the past, some traditional methods were applied to pick up the features from the signal which was disturbed by noises, but the transient signal can’t be identified. In this paper, an adaptive wavelet packet neural network (WPNN) method is presented for diagnosing amplifier fault based on pattern recognition at the first time. This method consists of two stages: firstly, the wavelet packet decomposition is used for feature extraction and secondly, a feed-forward neural network is utilized for pattern classification. Moreover, during the learning phase of WPNN, the wavelet packet entropy and the weights are updated adaptively to minimize the learning error. The experimental results show that the adaptive WPNN method is effective in detecting and diagnosing the amplifier fault of the missile.