Localization of Acoustic Emission Source Based on Chaotic Neural Networks
Aidong Deng, Xiaodan Zhang, Jianeng Tang, Zhao Li, Kang Qin · Applied Mathematics & Information Sciences · 2012
Because of containing several model waveforms and transmission speed of each model are various, the source signal of rub-impact acoustic emission (AE) will lead to waveform distortion in propagation process, and it is difficult to achiev e exact source location by traditional time difference of arrival algorit hm. A chaotic neural network technique was introduced to calculate the location of AE source. Numerous researches show that rotor rub-impact fault has sufficient non-linear features, so obtain the cha racteristics of the non-linear dynamics which reveal the AE source form the rub-impact data by using the chaos theory and use it as the input of the neural network to get the localization. We propose a modified Gaussian Mixed Model (GMM) with an embedded Time Delay Neural Network (TDNN). It integrates the merits of GMM and TDNN. Simulation results prove, theoretically and practically, tha t it can locate AE source efficiently and provide the basis for the rotor rub- impact fault diagnosis, so it has good application prospect and is worth to research further more.