Research of fault information fusion based on ICEEMDAN and PSO- SVM algorithm for rolling bearing diagnosis

Abstract

Abstract As a key component of mechanical system, the extraction and accurate identification of fault characteristic information of rolling bearing is very important to ensure its normal operation. The diagnosis accuracy is occasionally low due to the limitation of information collected by a single type of data source. In this paper, the bearing vibration signal and acoustic emission signal are employed as analysis sources, a novel method based on ICCEMDAN (improved complete ensemble empirical mode decomposition with adaptive noise) with optimized SVM (support vector machine) is presented for the fault information fusion, feature extraction, and fault pattern recognition of rolling bearing. Firstly, ICEEMDAN algorithm is developed to decompose the rolling bearing vibration signal and acoustic emission signal for a series of IMF (intrinsic mode function) components. Secondly, the valuable components that can characterize the original signal status are selected based on the correlation coefficient-variance contribution criterion. Thirdly, the singular spectral entropy of the reconstructed component is calculated as the eigenvalue and the two signal eigenvectors are fused as a new eigenvector set. Finally, the feature vector set is input into the optimized SVM classifier model based on PSO optimization for training and pattern recognition, in which the accuracy and efficiency of the classifier model and SVM classifier model are compared. Study of model simulation and fault simulation experiments show that the presented model based on the singular value entropy fusion of ICEEMDAN and PSO-SVM can effectively extract the fault characteristics of rolling bearing signals and has a desired performance in the accurate pattern recognition.