A novel compound neural network for fault sources recognition
Jiao Weidong, Qian Suxiang, Peng Lin, Ma Zewen, Yuan Qingping · 2010
Independent component analysis (ICA) is a powerful tool for redundancy reduction and nongaussian data analysis. And, artificial neural network (ANN), especially the self-organizing map (SOM) based on unsupervised learning is a kind of excellent method for pattern clustering and recognition. By combining ICA with ANN, a novel compound neural network for fault sources recognition was proposed. First, neural ICA algorithm was applied to fusion of multi-channel measurements by sensors. Moreover, further feature extraction was made. Thus, statistical features higher than second order were captured from the measurements. Second, a typical neural classifier such as the back-propagation (BP), the radial basis function (RBF) or the SOM network was trained for the final fault sources recognition. The results from contrast experiments in fault diagnosis of rotating machines show that the proposed compound neural network with ICA based feature extraction can recognize various fault sources at considerable accuracy, and be constructed in simpler way, both of which imply its great potential in fault diagnosis.