Adaptive Swarm Decomposition Algorithm for Compound Fault Diagnosis of Rolling Bearings

The feature extraction of compound faults is still considered the bottle neck task of machinery fault diagnosis. In this article, a novel adaptive swarm decomposition (ASWD) algorithm based on fine to coarse (FTC) segmentation is proposed for compound fault detection of rolling bearings. Firstly, the number of oscillating components that affects the results of ASWD is automatically determined by the order statistics filter and energy spectrum segmentation method without any prior knowledge. Secondly, the Teager energy kurtosis (TEK) of successively extracted components is employed as the indicator to evaluate the effectiveness of iterations. This not only setups the swarm decomposition (SWD) threshold, but also improves the performance of periodic impulses separation. Finally, ASWD is applied to intelligently separate the different oscillating components and suppress the redundant decomposition. The testing results of ASWD on the simulation and real cases indicate that ASWD can effectively extract compound fault impulses from multicomponent vibration signals. The comparison between SWD and other decomposition methods further verifies the superiority of ASWD. The characteristic frequency intensity coefficient (CFIC) of ASWD is increased by 34.2%, 49.2%, and 56.5% in the three cases, respectively, than SWD, variational mode decomposition (VMD), and ensemble empirical mode decomposition (EEMD).