A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Displacement Identification by Computer Vision for ConditionMonitoring of Rail Vehicle Bearings
Bearings of rail vehicles bear various dynamic forces. Any fault of the bearing seriouslythreatens running safety. For fault diagnosis, vibration and temperature measured from the bogieand acoustic signals measured from trackside are often used. However, installing additional sensingdevices on the bogie increases manufacturing cost while trackside monitoring is susceptible toambient noise. For other application, structural displacement based on computer vision is widelyapplied for deflection measurement and damage identification of bridges. This article proposesto monitor the health condition of the rail vehicle bearings by detecting the displacement of boltson the end cap of the bearing box. This study is performed based on an experimental platform ofbearing systems. The displacement is monitored by computer vision, which can image real-timedisplacement of the bolts. The health condition of bearings is reflected by the amplitude of thedetected displacement by phase correlation method which is separately studied by simulation. Toimprove the calculation rate, the computer vision only locally focuses on three bolts rather thanthe whole image. The displacement amplitudes of the bearing system in the vertical direction arederived by comparing the correlations of the image’s gray-level co-occurrence matrix (GLCM). Forverification, the measured displacement is checked against the measurement from laser displacementsensors, which shows that the displacement accuracy is 0.05 mm while improving calculation rate by68%. This study also found that the displacement of the bearing system increases with the increase inrotational speed while decreasing with static load ; QC 20210710
Displacement Identification by Computer Vision for ConditionMonitoring of Rail Vehicle Bearings
Bearings of rail vehicles bear various dynamic forces. Any fault of the bearing seriouslythreatens running safety. For fault diagnosis, vibration and temperature measured from the bogieand acoustic signals measured from trackside are often used. However, installing additional sensingdevices on the bogie increases manufacturing cost while trackside monitoring is susceptible toambient noise. For other application, structural displacement based on computer vision is widelyapplied for deflection measurement and damage identification of bridges. This article proposesto monitor the health condition of the rail vehicle bearings by detecting the displacement of boltson the end cap of the bearing box. This study is performed based on an experimental platform ofbearing systems. The displacement is monitored by computer vision, which can image real-timedisplacement of the bolts. The health condition of bearings is reflected by the amplitude of thedetected displacement by phase correlation method which is separately studied by simulation. Toimprove the calculation rate, the computer vision only locally focuses on three bolts rather thanthe whole image. The displacement amplitudes of the bearing system in the vertical direction arederived by comparing the correlations of the image’s gray-level co-occurrence matrix (GLCM). Forverification, the measured displacement is checked against the measurement from laser displacementsensors, which shows that the displacement accuracy is 0.05 mm while improving calculation rate by68%. This study also found that the displacement of the bearing system increases with the increase inrotational speed while decreasing with static load ; QC 20210710
Displacement Identification by Computer Vision for ConditionMonitoring of Rail Vehicle Bearings
Lei, Lei (author) / Dongli, Song (author) / Liu, Zhendong (author) / Xiao, Xu (author) / Zejun, Zheng (author)
2021-01-01
ISI:000652733700001
Article (Journal)
Electronic Resource
English
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