Accurate Detection Method of Aviation Bearing Based on Local Characteristics

Aviation bearing assembled detection is the final barrier to quality and safety. Therefore, an accurate detection method of aviation bearing that is based on local characteristics is designed to solve the detection problem of mis-assembly and miss-assembly of balls in aviation bearing assembled. Whe...

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Main Authors: Ping Xue, Yali Jiang, Hongmin Wang, Hai He
Format: Article
Language:English
Published: MDPI AG 2019-08-01
Series:Symmetry
Subjects:
Online Access:https://www.mdpi.com/2073-8994/11/9/1069
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spelling doaj-794a643d564e4701a962f2a371ce73992020-11-25T00:40:41ZengMDPI AGSymmetry2073-89942019-08-01119106910.3390/sym11091069sym11091069Accurate Detection Method of Aviation Bearing Based on Local CharacteristicsPing Xue0Yali Jiang1Hongmin Wang2Hai He3School of Automation, Harbin University of Science and Technology, Harbin150080, ChinaSchool of Automation, Harbin University of Science and Technology, Harbin150080, ChinaSchool of Automation, Harbin University of Science and Technology, Harbin150080, ChinaSchool of Automation, Harbin University of Science and Technology, Harbin150080, ChinaAviation bearing assembled detection is the final barrier to quality and safety. Therefore, an accurate detection method of aviation bearing that is based on local characteristics is designed to solve the detection problem of mis-assembly and miss-assembly of balls in aviation bearing assembled. When considering the spatial limitation of aviation bearing assembled image acquisition, the dynamic distribution of balls and the interference of lubricating grease on the surface, a dynamic local ball segmentation model that is based on U-Net network with symmetrical structure is designed to achieve the accurate segmentation of the local ball region of aviation bearing. Subsequently, an incomplete circle fitting algorithm is designed based on the segmented local ball image and Hough transform principle. These two algorithms make the measurement error of aviation bearing ball size less than 100 μm. Using bearings validates the algorithm. The results show that the accuracy of dynamic local ball segmentation model that is based on U-Net network with symmetrical structure is over 99%. At the same time, on the basis of accurate segmentation in aviation bearing local ball, the designed Hough circle algorithm is used for circle detection. The experimental results show that the false detection rate of mis-assembly and miss-assembly of balls is less than 3%. Further, the goal of zero-missed detection of mis-assembly and miss-assembly of balls in aviation bearing is achieved. The accurate segmentation of aviation bearing local ball and the effective identification of mis-assembly and miss-assembly of balls are realized. This method can provide a theory for the improvement of mis-assembly and miss-assembly of balls detection in aviation bearing. Furthermore, it has high application value.https://www.mdpi.com/2073-8994/11/9/1069aviation bearinglocal characteristicsU-Net networkHough transform
collection DOAJ
language English
format Article
sources DOAJ
author Ping Xue
Yali Jiang
Hongmin Wang
Hai He
spellingShingle Ping Xue
Yali Jiang
Hongmin Wang
Hai He
Accurate Detection Method of Aviation Bearing Based on Local Characteristics
Symmetry
aviation bearing
local characteristics
U-Net network
Hough transform
author_facet Ping Xue
Yali Jiang
Hongmin Wang
Hai He
author_sort Ping Xue
title Accurate Detection Method of Aviation Bearing Based on Local Characteristics
title_short Accurate Detection Method of Aviation Bearing Based on Local Characteristics
title_full Accurate Detection Method of Aviation Bearing Based on Local Characteristics
title_fullStr Accurate Detection Method of Aviation Bearing Based on Local Characteristics
title_full_unstemmed Accurate Detection Method of Aviation Bearing Based on Local Characteristics
title_sort accurate detection method of aviation bearing based on local characteristics
publisher MDPI AG
series Symmetry
issn 2073-8994
publishDate 2019-08-01
description Aviation bearing assembled detection is the final barrier to quality and safety. Therefore, an accurate detection method of aviation bearing that is based on local characteristics is designed to solve the detection problem of mis-assembly and miss-assembly of balls in aviation bearing assembled. When considering the spatial limitation of aviation bearing assembled image acquisition, the dynamic distribution of balls and the interference of lubricating grease on the surface, a dynamic local ball segmentation model that is based on U-Net network with symmetrical structure is designed to achieve the accurate segmentation of the local ball region of aviation bearing. Subsequently, an incomplete circle fitting algorithm is designed based on the segmented local ball image and Hough transform principle. These two algorithms make the measurement error of aviation bearing ball size less than 100 μm. Using bearings validates the algorithm. The results show that the accuracy of dynamic local ball segmentation model that is based on U-Net network with symmetrical structure is over 99%. At the same time, on the basis of accurate segmentation in aviation bearing local ball, the designed Hough circle algorithm is used for circle detection. The experimental results show that the false detection rate of mis-assembly and miss-assembly of balls is less than 3%. Further, the goal of zero-missed detection of mis-assembly and miss-assembly of balls in aviation bearing is achieved. The accurate segmentation of aviation bearing local ball and the effective identification of mis-assembly and miss-assembly of balls are realized. This method can provide a theory for the improvement of mis-assembly and miss-assembly of balls detection in aviation bearing. Furthermore, it has high application value.
topic aviation bearing
local characteristics
U-Net network
Hough transform
url https://www.mdpi.com/2073-8994/11/9/1069
work_keys_str_mv AT pingxue accuratedetectionmethodofaviationbearingbasedonlocalcharacteristics
AT yalijiang accuratedetectionmethodofaviationbearingbasedonlocalcharacteristics
AT hongminwang accuratedetectionmethodofaviationbearingbasedonlocalcharacteristics
AT haihe accuratedetectionmethodofaviationbearingbasedonlocalcharacteristics
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