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      Displacement Identification by Computer Vision for Condition Monitoring of Rail Vehicle Bearings

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          Abstract

          Bearings of rail vehicles bear various dynamic forces. Any fault of the bearing seriously threatens running safety. For fault diagnosis, vibration and temperature measured from the bogie and acoustic signals measured from trackside are often used. However, installing additional sensing devices on the bogie increases manufacturing cost while trackside monitoring is susceptible to ambient noise. For other application, structural displacement based on computer vision is widely applied for deflection measurement and damage identification of bridges. This article proposes to monitor the health condition of the rail vehicle bearings by detecting the displacement of bolts on the end cap of the bearing box. This study is performed based on an experimental platform of bearing systems. The displacement is monitored by computer vision, which can image real-time displacement of the bolts. The health condition of bearings is reflected by the amplitude of the detected displacement by phase correlation method which is separately studied by simulation. To improve the calculation rate, the computer vision only locally focuses on three bolts rather than the whole image. The displacement amplitudes of the bearing system in the vertical direction are derived by comparing the correlations of the image’s gray-level co-occurrence matrix (GLCM). For verification, the measured displacement is checked against the measurement from laser displacement sensors, which shows that the displacement accuracy is 0.05 mm while improving calculation rate by 68%. This study also found that the displacement of the bearing system increases with the increase in rotational speed while decreasing with static load.

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          Blind identification of full-field vibration modes from video measurements with phase-based video motion magnification

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            Structural Displacement Measurement Using an Unmanned Aerial System: Structural displacement measurement using an UAS

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              A Review of Machine Vision-Based Structural Health Monitoring: Methodologies and Applications

              In the past two decades, a significant number of innovative sensing and monitoring systems based on the machine vision-based technology have been exploited in the field of structural health monitoring (SHM). This technology has some inherent distinctive advantages such as noncontact, nondestructive, long distance, high precision, immunity to electromagnetic interference, and large-range and multiple-target monitoring. A lot of machine vision-based structural dynamic measurement and structural state inspection methods have been proposed. Real-world applications are also carried out to measure the structural physical parameters such as the displacement, strain/stress, rotation, vibration, crack, and spalling. The purpose of this review article is devoted to presenting a summary of the basic theories and practical applications of the machine vision-based technology employed in structural monitoring as well as its systematic error sources and integration with other modern sensing techniques.
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                Author and article information

                Contributors
                Role: Academic Editor
                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                17 March 2021
                March 2021
                : 21
                : 6
                : 2100
                Affiliations
                [1 ]State Key Laboratory of Traction Power, Southwest Jiaotong University, Chengdu 610036, China; leilei309@ 123456my.swjtu.edu.cn (L.L.); XuXiao@ 123456my.swjtu.edu.cn (X.X.); zhengjuner@ 123456my.swjtu.edu.cn (Z.Z.)
                [2 ]Department of Engineering Mechanics, KTH Royal Institute of Technology, 10044 Stockholm, Sweden; zhendong@ 123456kth.se
                Author notes
                [* ]Correspondence: sdlcds@ 123456swjtu.edu.cn ; Tel.: +86-028-8646-6026
                Author information
                https://orcid.org/0000-0001-7393-569X
                https://orcid.org/0000-0002-0369-5667
                Article
                sensors-21-02100
                10.3390/s21062100
                8002472
                60ae4f62-5b64-471d-9935-45d6183aab3d
                © 2021 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 03 February 2021
                : 10 March 2021
                Categories
                Article

                Biomedical engineering
                displacement detection,bearing system,experimental platform,computer vision,phase correlation,glcm,condition monitoring

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