基于VMD与FastICA的电动汽车减速器噪声测试分离技术研究

Research on Background Noise Separation of Reducers Based on VMD and FastICA

电动汽车NVH噪声性能极大影响驾乘的舒适性,NVH性能已经成为验证其产品质量的关键指标,加强对噪声测试技术的研究,可以极大程度地推动减速器的高质量发展。针对噪声测试台架的噪声数据采集环境干扰大且难以消除的问题,提出一种基于变分模态分解(Variational Modal Decomposition,VMD)与快速独立成分分析(Fast Independent Component Analysis,FastICA)的噪声信号分解及基于信号互相关分析的背景噪声成分识别与降噪方法。首先,采用VMD算法将减速器混合信号分解为多个模态分量;其次,将获得的模态分量通过FastICA算法进行进一步分离解混,获得多维独立分量;最后,利用信号的互相关性分析对多维独立分量进行识别与滤除降噪,完成减速器噪声测试的背景噪声的分离。结果表明,提出的VMD-FastICA能正确分离出混合信号中的背景噪声信号,有效进行噪声分离;通过减速器噪声测试台架9000 r/min转速工况的运转试验对比,分解滤除背景噪声后声压级平均降低了3.09 dB(A),平均误差为0.32 dB(A)。研究进一步优化了新能源汽车减速器噪声性能测试方法,为提升减速器噪声测试的精度提供了技术保障。

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 9000 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.