Research on background noise separation of speed reducer based on WOA optimized VMD

The NVH (Noise, Vibration, Harshness) of electric vehicles greatly affects driving comfort, and the NVH has become a key indicator for verifying the quality of their products, strengthening the research on noise testing technology can greatly promote the high-quality development of reducers. Given the problem that the noise data acquisition environment of the noise test stand is highly disturbed and difficult to eliminate, this paper proposes a whale optimization algorithm (WOA) with envelope entropy as the fitness function to carry out the improved variational modal decomposition (VMD) to decompose the noise signal of the speed reducer to obtain the IMF component, and to use the signal inter-correlation analysis of the IMF background noise to identify and filter out the noise reduction, and to complete the separation of the noise from the background noise of the speed reducer. The method is based on the iterative optimization of the number of decomposition layers and penalty factor of the VMD by WOA to find out the optimal combination of decomposition parameters and identify and reduce the background noise components based on the signal correlation analysis. Firstly, it is verified that the method has more effective decomposition performance than the traditional VMD algorithm by simulation signal test. Then the method is used for the background noise separation of electric vehicle gearbox, and the results show that the WOA-VMD proposed in this paper correctly separates the background noise signals in the mixed signals, and can effectively carry out the noise separation, and the decomposition filters out the background noise with an average reduction of the sound pressure level of 2.99 dB(A), and the average error of the sound pressure level with the baseline condition is 0.45 dB(A). Also, the noise reduction method using WOA-VMD reduces the sound pressure level by an average of 1.47 dB(A) than that obtained by the VMD algorithm, which has a better noise separation effect. The above conclusion proves that the present method improves the accuracy of noise testing of electric vehicle reducers.