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基于激光线扫描器的 U 型波纹管卷积节距检测系统。

Detection System for U-Shaped Bellows Convolution Pitches Based on a Laser Line Scanner.

机构信息

College of Mechanical and Electrical Engineering, Nanjing Forestry University, Nanjing 210037, China.

Department of Mechanical Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada.

出版信息

Sensors (Basel). 2020 Feb 15;20(4):1057. doi: 10.3390/s20041057.

DOI:10.3390/s20041057
PMID:32075301
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7070915/
Abstract

An expansion joint is mainly composed of bellows and other components; it is attached on a container shell or pipe to compensate for the additional stress caused by temperature differences and mechanical vibrations. In China, the expansion joint fatigue tests are often used to assess the quality of products. After fatigue tests, convolution pitch will be changed. The amount of change is an important index that can be used to evaluate bellows expansion joints. However, the convolution pitch detection is mainly done manually and randomly by inspection agencies before shipping to the end users. This common practice is not efficient and is often subjective. This paper introduced a novel method for automatically detecting the change of the convolution pitch based on a laser line scanner and data processing technology. The laser line scanner is combined with a precision motorized stage to obtain the point cloud data of the bellows. After denoising and fitting, a peak-finding algorithm is applied to search for the crest of a convolution. The method to find the convolution pitch and the decision that needs to be made to ensure product eligibility are described in detail. A DN500 expansion joint is used as a sample to illustrate the efficiency of the system. The application of the technique intuitively allows a higher precision and relative efficiency in quality inspection of bellows expansion joints. It has also been implemented in the Special Equipment Safety Supervision and Inspection Institute of Jiangsu province with great success.

摘要

膨胀节主要由波纹管及其他部件组成;它连接在容器外壳或管道上,以补偿因温差和机械振动而产生的附加应力。在中国,膨胀节疲劳试验常被用于评估产品质量。疲劳试验后,波纹管的波纹间距会发生变化。变化的幅度是评估波纹管膨胀节的一个重要指标。然而,在发货给最终用户之前,检验机构主要通过手动和随机的方式对波纹间距进行检测。这种常见的做法效率不高,而且往往具有主观性。本文介绍了一种基于激光线扫描器和数据处理技术自动检测波纹间距变化的新方法。激光线扫描器与精密电机驱动平台相结合,获取波纹管的点云数据。经过去噪和拟合后,应用峰查找算法搜索到一个波纹的波峰。详细描述了寻找波纹间距的方法以及为确保产品合格所需做出的决策。以一个 DN500 的膨胀节作为样本,说明了该系统的效率。该技术的应用直观地提高了波纹管膨胀节质量检验的精度和相对效率。它已在江苏省特种设备安全监督检验研究院成功实施。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/aa78/7070915/97ca0e148fcf/sensors-20-01057-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/aa78/7070915/7d54edf8a2f0/sensors-20-01057-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/aa78/7070915/81d6d8195b04/sensors-20-01057-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/aa78/7070915/2bf137e1ff53/sensors-20-01057-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/aa78/7070915/fced6a977dba/sensors-20-01057-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/aa78/7070915/9cbce12482f6/sensors-20-01057-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/aa78/7070915/9edf4d03508a/sensors-20-01057-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/aa78/7070915/6058868d54ac/sensors-20-01057-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/aa78/7070915/d284362d439a/sensors-20-01057-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/aa78/7070915/8a54cc6f6b8d/sensors-20-01057-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/aa78/7070915/07a6bce02689/sensors-20-01057-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/aa78/7070915/8bf6f283c617/sensors-20-01057-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/aa78/7070915/97ca0e148fcf/sensors-20-01057-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/aa78/7070915/7d54edf8a2f0/sensors-20-01057-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/aa78/7070915/81d6d8195b04/sensors-20-01057-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/aa78/7070915/2bf137e1ff53/sensors-20-01057-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/aa78/7070915/fced6a977dba/sensors-20-01057-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/aa78/7070915/9cbce12482f6/sensors-20-01057-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/aa78/7070915/9edf4d03508a/sensors-20-01057-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/aa78/7070915/6058868d54ac/sensors-20-01057-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/aa78/7070915/d284362d439a/sensors-20-01057-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/aa78/7070915/8a54cc6f6b8d/sensors-20-01057-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/aa78/7070915/07a6bce02689/sensors-20-01057-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/aa78/7070915/8bf6f283c617/sensors-20-01057-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/aa78/7070915/97ca0e148fcf/sensors-20-01057-g012.jpg

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本文引用的文献

1
An Improved Low-Noise Processing Methodology Combined with PCL for Industry Inspection Based on Laser Line Scanner.一种基于激光线扫描仪的、结合点云库(PCL)用于工业检测的改进型低噪声处理方法。
Sensors (Basel). 2019 Aug 2;19(15):3398. doi: 10.3390/s19153398.
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通过结合基于连续小波变换的模式匹配改进质谱中的峰检测。
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