Name
Title | ||
|---|---|---|
Displacement Identification By Computer Vision For Condition Monitoring Of Rail Vehicle Bearings |
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. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.3390/s21062100 | SENSORS |
Keywords | DocType | Volume |
displacement detection, bearing system, experimental platform, computer vision, phase correlation, GLCM, condition monitoring | Journal | 21 |
Issue | ISSN | Citations |
6 | 1424-8220 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lei Lei | 1 | 0 | 0.34 |
| Dongli Song | 2 | 0 | 0.34 |
| Zhendong Liu | 3 | 0 | 0.34 |
| Xiao Xu | 4 | 0 | 0.34 |
| Zejun Zheng | 5 | 0 | 0.34 |