Early intelligent fault diagnosis of rotating machinery based on IWOA-VMD and DMKELM

Abstract

The effect of early fault vibration signals from rotating machinery is weak and easily drowned out by intense noise. Therefore, it is still a great challenge to make early fault diagnosis. An intelligent early fault diagnosis method for rotating machinery is proposed based on the parameter optimization of the variational mode decomposition (VMD) and deep multi-kernel extreme learning machine (DMKELM). Firstly, the improved whale optimization algorithm (IWOA) is designed by introducing the iterative chaotic mapping, nonlinear convergence factor and inertia weight to optimize the VMD parameters. Secondly, the optimized VMD (OVMD) with sample entropy is created to reduce noise and reconstruct the signals. Finally, the radial basis kernel function (RBF) and polynomial kernel (PK) are introduced to construct the mixed kernel function, which can enhance the classification performance and generalization ability of the model. Two experiments on bearings and gears show that the fault diagnosis accuracy by DMKELM is 99 and 98.5%, respectively, which is at least 1% higher than comparative methods and increases by 4% after noise reduction. The result shows that the proposed method has great superiority in the early fault diagnosis of rotating machinery.