Research on Background Noise Separation of Reducers Based on VMD and FastICA - Li Weilin; Jiang Yangming; Zhang Zhongjie; Liu Ting; Lin Ting; Cheng Xu; Chen Feng; Wang Keyu; Zeng Hao (Jixie chuandong)
The NVH noise performance of electric vehicles greatly affects driving comfort, and the NVH performance has become a key indicator for verifying the quality of their products; strengthening the research can greatly promote the high-quality development of reducers. Aiming at the problem that the noise data acquisition environment of the noise test stand is highly disturbed and difficult to be eliminated, a noise signal decomposition based on variational modal decomposition (VMD) and fast independent component analysis (FastICA) and a background noise component identification and noise reduction method based on signal correlation analysis were proposed. The method firstly adopted the VMD to decompose the speed reducer mixed signal into multiple modal components, and then further separated and demixed the obtained modal components through the FastICA to obtain the multidimensional independent components. Finally, the multidimensional independent components were identified and filtered out by using the signal correlation analysis to reduce the noise and complete the separation of the background noise in the test. The results show that the proposed VMD-FastICA can correctly separate the background noise signal in the mixed signal, and effectively separate the noise. Through the reducer noise test stand 9 000 r/min speed conditions of operation test comparison, the decomposition filters out the background noise and reduces the sound pressure level by an average of 3.09 dB(A), with an average error of 0.32 dB(A), to further optimize new energy vehicle gearbox noise performance test methods, and to enhance the accuracy of the gearbox noise test to provide a technical guarantee.