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基于 ICA 模型的轮式装载机齿轮泵降噪研究。

A Study on Noise Reduction of Gear Pumps of Wheel Loaders Based on the ICA Model.

机构信息

School of Mechanical Engineering, Shandong University, Jinan 250061, China.

Key Laboratory of High Efficiency and Clean Mechanical Manufacture, Shandong University, Ministry of Education, Jinan 250061, China.

出版信息

Int J Environ Res Public Health. 2019 Mar 19;16(6):999. doi: 10.3390/ijerph16060999.

DOI:10.3390/ijerph16060999
PMID:30893941
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6466413/
Abstract

In order to reduce the noise level of wheel loaders caused by gear pumps and realize environmentally sustainable development, the noise generation mechanism of a gear pump was studied, and the influence of flow pulsation and gear impact on noise was analyzed. In order to reduce the interference of other noise sources on the noise level of the gear pump, a noise test rig was established. The mixed noise signals obtained from the rig test were separated using the ICA model. The ICA model includes the following algorithms: The fast Fourier transform (FFT), independent component analysis (ICA) and inverse fast Fourier transform (IFFT). Some theories about the influence of the teeth number and teeth profile on noise were analyzed by theory and simulation. A noise reduction strategy was proposed by increasing the teeth numbers and modifying the teeth profile of the gear pump. The tests results showed that the noise values of the external and the driver's ear of the wheel loader were reduced to 1 and 2.2 dB (A), respectively. This proves the effectiveness of the optimization scheme of gear pump noise reduction.

摘要

为了降低轮式装载机齿轮泵的噪声水平，实现环境的可持续发展，研究了齿轮泵的噪声产生机理，分析了流量脉动和齿轮冲击对噪声的影响。为了降低其他噪声源对齿轮泵噪声水平的干扰，建立了噪声试验台。利用 ICA 模型对从试验台上获得的混合噪声信号进行分离。ICA 模型包括以下算法：快速傅里叶变换（FFT）、独立成分分析（ICA）和逆快速傅里叶变换（IFFT）。通过理论和仿真分析了齿数和齿廓对噪声的影响。提出了通过增加齿轮泵的齿数和修改齿廓来降低噪声的策略。试验结果表明，轮式装载机外部和驾驶员耳部的噪声值分别降低到 1 和 2.2 dB（A）。这证明了齿轮泵降噪优化方案的有效性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d73a/6466413/98ad7990c78d/ijerph-16-00999-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d73a/6466413/9a883d70384c/ijerph-16-00999-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d73a/6466413/3e460b87b7a0/ijerph-16-00999-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d73a/6466413/d491144bfdb7/ijerph-16-00999-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d73a/6466413/cfff23fa72db/ijerph-16-00999-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d73a/6466413/fd21bb3aad0b/ijerph-16-00999-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d73a/6466413/33493071da89/ijerph-16-00999-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d73a/6466413/d0230537506d/ijerph-16-00999-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d73a/6466413/6bffe0e6fc74/ijerph-16-00999-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d73a/6466413/3fe5a3cb7ca3/ijerph-16-00999-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d73a/6466413/a6f825c2858c/ijerph-16-00999-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d73a/6466413/98ad7990c78d/ijerph-16-00999-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d73a/6466413/9a883d70384c/ijerph-16-00999-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d73a/6466413/3e460b87b7a0/ijerph-16-00999-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d73a/6466413/d491144bfdb7/ijerph-16-00999-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d73a/6466413/cfff23fa72db/ijerph-16-00999-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d73a/6466413/fd21bb3aad0b/ijerph-16-00999-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d73a/6466413/33493071da89/ijerph-16-00999-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d73a/6466413/d0230537506d/ijerph-16-00999-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d73a/6466413/6bffe0e6fc74/ijerph-16-00999-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d73a/6466413/3fe5a3cb7ca3/ijerph-16-00999-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d73a/6466413/a6f825c2858c/ijerph-16-00999-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d73a/6466413/98ad7990c78d/ijerph-16-00999-g011.jpg

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