On line fault diagnosis of rolling bearings based on feature fusion
Guowen Wu, Yangyang Tian, Wentao Mao · 2018
With the rapid development of the Internet of things and other advanced sensing technologies, the demand for real-time fault diagnosis of bearings is increasing a lot. In order to improve the real-time and numerical stability of on-line fault diagnosis of bearings, an online diagnosis method based on feature fusion is proposed in this paper. The method uses Kernel Principal Component Analysis (KPCA) to fuse feature combinations extracted by Empirical Mode Decomposition(EMD) and Wavelet Packet Transform(WPT), and uses the online extreme learning machine to construct the online diagnosis model. The whole process can be divided into offline stage and online stage: in off-line stage, combining EMD and WPT features under different fault status, the kernel feature vectors are obtained by KPCA, and then the initial online extreme learning machine model is constructed; In online stage, EMD and WPT are firstly used to extract features for sequentially arrived raw signals, and according to the feature's weights which are obtained in offline stage, the new features are reduced directly and the online extreme learning machine model is updated adaptively. The experimental results on the IMS bearing data set show that the method can make full use of the complementary characteristics of different information, and effectively improve the accuracy and stability of online diagnosis without increasing the reaction time.