Application of variational mode decomposition optimized with improved whale optimization algorithm in bearing failure diagnosis

The vibration signals of rolling bearings are unstable and nonlinear, with weak information on failure features and extremely low signal-to-noise ratio (SNR). To solve these problems, this paper presents a failure diagnosis method based on variational mode decomposition (VMD) optimized by the improved whale optimization algorithm (IWOA) is proposed, and verifies the effectiveness of the method through a failure experiment on a test stand. Specifically, the whale optimization algorithm (WOA) was improved by replacing the linear parameter a1 with a nonlinear rule. The replacement effectively improves the solution accuracy, and convergence speed. Next, the VMD parameters were optimized with IWOA. Experimental results show that the VMD optimized with IWOA can effectively and easily extract the early failure features of rolling bearings by enhancing the weak information on failure features.