Wavelet neural network based transient fault signal detection and identification

Wei-rong Chen, Qing-quan Qian, Xiao-Ru Wang
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引用次数: 10

Abstract

This paper proposes a novel approach to detect and identify transient fault signals. Because the fault signals are non-stationary transient ones, the traditional signal analysis methods, such as the FFT, are not so efficient and useful for fault signal detection. A wavelet neural network (WNN) is used to extract the signal features, and then a feedforward neural network (FNN) is used to identify and classify these features to detect the fault signals. The simulation shows that this method is suitable for application of transient fault detection.
基于小波神经网络的暂态故障信号检测与识别
提出了一种检测和识别暂态故障信号的新方法。由于故障信号是非平稳暂态信号,传统的信号分析方法,如FFT,在故障信号检测中效率不高。首先利用小波神经网络(WNN)提取信号特征,然后利用前馈神经网络(FNN)对这些特征进行识别和分类,从而检测出故障信号。仿真结果表明,该方法适用于暂态故障检测。
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