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声发射检测在低速重载齿轮故障诊断中的研究与应用。

Study and application of acoustic emission testing in fault diagnosis of low-speed heavy-duty gears.

机构信息

Key Laboratory of Advanced Manufacturing Technology, Beijing University of Technology, Chao Yang District, Beijing, 100124, China.

出版信息

Sensors (Basel). 2011;11(1):599-611. doi: 10.3390/s110100599. Epub 2011 Jan 10.

DOI:10.3390/s110100599
PMID:22346592
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3274124/
Abstract

Most present studies on the acoustic emission signals of rotating machinery are experiment-oriented, while few of them involve on-spot applications. In this study, a method of redundant second generation wavelet transform based on the principle of interpolated subdivision was developed. With this method, subdivision was not needed during the decomposition. The lengths of approximation signals and detail signals were the same as those of original ones, so the data volume was twice that of original signals; besides, the data redundancy characteristic also guaranteed the excellent analysis effect of the method. The analysis of the acoustic emission data from the faults of on-spot low-speed heavy-duty gears validated the redundant second generation wavelet transform in the processing and denoising of acoustic emission signals. Furthermore, the analysis illustrated that the acoustic emission testing could be used in the fault diagnosis of on-spot low-speed heavy-duty gears and could be a significant supplement to vibration testing diagnosis.

摘要

大多数目前关于旋转机械声发射信号的研究都是以实验为导向的，很少有涉及现场应用的。在本研究中，提出了一种基于内插细分原理的冗余第二代小波变换方法。使用该方法，在分解过程中不需要细分。近似信号和细节信号的长度与原始信号的长度相同，因此数据量是原始信号的两倍；此外，数据冗余特性也保证了该方法的出色分析效果。现场低速重载齿轮故障的声发射数据分析验证了冗余第二代小波变换在声发射信号处理和去噪中的应用。此外，分析表明，声发射测试可用于现场低速重载齿轮的故障诊断，并且可以成为振动测试诊断的重要补充。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6471/3274124/db0f85326f3d/sensors-11-00599f11.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6471/3274124/5954e2d2e388/sensors-11-00599f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6471/3274124/158e22f88ff6/sensors-11-00599f5.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6471/3274124/8d4dd5d3f32f/sensors-11-00599f9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6471/3274124/54e206fe0ea0/sensors-11-00599f10a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6471/3274124/db0f85326f3d/sensors-11-00599f11.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6471/3274124/d8998ab4c14c/sensors-11-00599f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6471/3274124/7cb3328b6b37/sensors-11-00599f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6471/3274124/98641b178bde/sensors-11-00599f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6471/3274124/5954e2d2e388/sensors-11-00599f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6471/3274124/158e22f88ff6/sensors-11-00599f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6471/3274124/a80dbb354779/sensors-11-00599f6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6471/3274124/1dd0f4f28583/sensors-11-00599f7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6471/3274124/c9285e115789/sensors-11-00599f8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6471/3274124/8d4dd5d3f32f/sensors-11-00599f9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6471/3274124/54e206fe0ea0/sensors-11-00599f10a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6471/3274124/db0f85326f3d/sensors-11-00599f11.jpg

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