Title :
Wavelet neural network based transient fault signal detection and identification
Author :
Chen, Wei-rong ; Qian, Qing-Quan ; Wang, Xiao-Ru
Author_Institution :
Inst. of Electr. & Autom., Southwest Jiaotong Univ., Chengdu, China
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
Keywords :
fault diagnosis; feature extraction; feedforward neural nets; identification; signal detection; transient analysis; wavelet transforms; feature classification; feedforward neural network; nonstationary signals; signal analysis methods; signal feature extraction; signal identification; simulation; transient fault signal detection; wavelet neural network; Computer vision; Fault detection; Fault diagnosis; Feature extraction; Feedforward neural networks; Neural networks; Signal analysis; Signal detection; Signal processing; Transient analysis;
Conference_Titel :
Information, Communications and Signal Processing, 1997. ICICS., Proceedings of 1997 International Conference on
Print_ISBN :
0-7803-3676-3
DOI :
10.1109/ICICS.1997.652215