Robust Fault Diagnosis of Rolling Bearings Using Multivariate Intrinsic Multiscale Entropy Analysis and Neural Network Under Varying Operating Conditions

olling bearings are crucial components in mechanical, civil and aerospace engineering. The practical working conditions of rolling bearings are complex and tough, hence fault diagnosis of rolling bearings under varying operating conditions is very challenging. This paper proposes a robust fault diagnosis approach of rolling bearings using multivariate intrinsic multiscale entropy analysis and neural network under varying operating conditions. The proposed approach deals with multivariate signal collected from multi-sensor acquisition system to capture much dynamical characteristic information. Multivariate intrinsic multiscale entropy analysis consists of adaptive projection intrinsically transformed multivariate empirical mode decomposition with adaptive noise (APIT-MEMD-AN) and improved multivariate multiscale sample entropy (IMMSE) with smoothed coarse graining process. Intrinsic mode functions (IMFs) obtained by APIT-MEMD-AN depict dynamical properties of multivariate signals. IMMSE of certain orders IMFs are adopted as input values of back propagation (BP) neural network to achieve fault classification of rolling bearings. APIT-MEMD-AN and IMMSE endow the proposed approach with the underlying adaptivity and robustness, making the proposed approach a fully data driven and robust method. Theoretical derivations, numerical simulations and experimental results verify the effectiveness and superiority of the proposed approach. The research work demonstrates the proposed approach is promising in fault diagnosis of rotary machinery under varying operating conditions.