IVMD融合RobustICA的内燃机噪声源分离
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TK402

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国家重点研发计划资助项目 (2017YFC0211301)


Noise Source Separation of Internal Combustion Engine Based on IVMD-RobustICA Method
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    摘要:

    为了准确分离识别内燃机的主要噪声源,提出了一种改进变分模态分解融合鲁棒独立分量分析的方法。首先,针对变分模态分解方法的分解数选择问题进行了算法优化,提出了基于重构信号能量比和中心频率的改进变分模态分解方法,并利用仿真信号进行了验证;其次,进行了内燃机噪声试验,利用改进变分模态分解将单通道信号分解成多个信号分量,根据信号分量与源信号的互信息主要分量识别,克服了主要噪声分量选择客观依据不足的问题;最后,通过鲁棒独立分量分析提取主要噪声分量的独立成分,并结合相干分析和时频分析进行噪声源识别。结果显示,所提出的方法能够有效进行噪声源分离,可成功识别出燃烧噪声、活塞敲击噪声和空压机噪声等内燃机主要噪声源。

    Abstract:

    In order to accurately separate and identify the main noise sources of internal combustion engines, a method based on IVMD-RobustICA (improved variational mode decomposition, robust independent component analysis) is proposed. Firstly, a self-adaption algorithm is proposed to determine the decomposition number of variational mode decomposition. An IVMD method based on reconstructed signal energy ratio and center frequency is developed. Then the single channel signal of measured engine noise is decomposed into multiple components by IVMD. The main components are identified according to the mutual information (MI) between signal components and the source signal. Finally, the independent components are extracted by RobustICA, and the noise sources are identified by coherent analysis and time-frequency analysis. The results show that the proposed method can separate and identify the main noise sources, such as the combustion noise, the piston slap noise, and the air compressor noise.

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  • 在线发布日期: 2020-03-17
  • 出版日期: 2020-02-28
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