The belt conveyor sound denoising method based on MEPSO-VMD and improved wavelet threshold

Abstract

The sound signals of belt conveyors contain rich state information, and accurately extracting fault signals from non-stationary signals such as noise is one of the key technologies for solving belt conveyor fault identification. To address this issue, a denoising method based on Motion -Encoding Particle Swarm Optimization and Variational Mode Decomposition is proposed, combined with Sample Entropy and an improved wavelet threshold for signal screening and filtering. First, with the minimum fitness function value as the target, the optimization algorithm is used to search for the VMD parameters K and α adaptively. Then, the intrinsic mode functions decomposed by VMD are filtered based on their Sample Entropy values. Finally, the IMF components that meet the conditions are decomposed by wavelet threshold for secondary denoising. Since fault signals have weak and nonlinear characteristics, the proposed algorithm effectively extracts weak sound signals from a noisy background. Experimental results show that the proposed algorithm improves the signal-to-noise ratio by approximately 20% compared to traditional denoising methods. In addition, it is further applied to motor bearing fault diagnosis, improving diagnostic efficiency and providing a useful reference for monitoring of rotating machinery, motor systems, and power equipment.