Research on a Sliding Detection Method for an Elevator Traction Wheel Based on Machine Vision
Abstract
:1. Introduction
2. Slip Detection Scheme
2.1. Slip Detection Method
- (1)
- Make a white mark on the edge of the traction wheel and on the same position of the wire rope.
- (2)
- Collect the slippage image and use the image processing algorithm to obtain the offset distance between the centroid of the two white markers in the circumference direction ∆y. When ∆y < ε, the marker is qualified; otherwise, the marker is not qualified, and it needs to be marked again. ε is the preset minimum offset distance.
- (3)
- When the elevator runs a round trip and produces slippage, the white mark on the edge of the traction wheel and the wire rope will be misaligned. The original image of the mark will be collected by the CCD camera located directly above the traction wheel.
- (4)
- For the original image, the improved nonlinear geometric transformation algorithm is used to transform the sliding image with different object distances into the target image with the same object distances.
- (5)
- For the target image, the slippage detection algorithm is used to obtain the distance of the marker in the horizontal direction, and the slippage of the elevator traction wheel is calculated by using the calibrated pixel equivalent and the initial slip distance.
2.2. The Experiment Scheme
3. Slip Detection Algorithm
3.1. Nonlinear Geometric Transformation of Images
3.2. Image Processing Algorithm for Slip Detection
4. Camera Calibration and the Building of the Experimental Platform
4.1. Camera Calibration
4.2. Building the Experimental Platform
5. Test Result Analysis
5.1. On-Site Testing
5.2. Error and Measurement Uncertainty Analysis
5.2.1. Error Analysis
5.2.2. Measurement Uncertainty Analysis
6. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
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Group Number | The Real Slippage/mm | Single Pixel Distance/pixel | Average Pixel Distance/pixel | Detection of Slippage/mm | Absolute Error/mm | Fractional Error/% | Residual Error/mm |
---|---|---|---|---|---|---|---|
1 | 33.93 | 41 | 40.0 | 35.6 | 1.67 | 4.9% | 1.8 |
40 | |||||||
39 | |||||||
2 | 34.34 | 37 | 37.0 | 33.0 | −1.32 | −3.8% | −0.7 |
36 | |||||||
38 | |||||||
3 | 34.91 | 37 | 37.3 | 33.3 | −1.60 | −4.6% | −0.4 |
40 | |||||||
35 | |||||||
4 | 32.99 | 40 | 38.7 | 34.5 | 1.46 | 4.4% | 0.7 |
37 | |||||||
39 | |||||||
5 | 31.96 | 36 | 37.0 | 33.0 | 1.06 | 3.3% | −0.7 |
37 | |||||||
38 | |||||||
6 | 32.42 | 36 | 36.3 | 32.4 | 0.03 | 0.1% | −1.3 |
37 | |||||||
36 | |||||||
7 | 32.48 | 39 | 37.3 | 33.3 | 0.83 | 2.5% | −0.4 |
37 | |||||||
36 | |||||||
8 | 32.47 | 38 | 39.7 | 35.3 | 2.84 | 8.8% | 1.6 |
41 | |||||||
40 | |||||||
9 | 32.02 | 39 | 37.3 | 33.3 | 1.29 | 4.0% | −0.4 |
37 | |||||||
36 | |||||||
Average value | 33.06 | 37.9 | 37.9 | 33.8 | 0.74 | 2.2% |
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Chen, J.; Jing, L.; Hong, T.; Liu, H.; Glowacz, A. Research on a Sliding Detection Method for an Elevator Traction Wheel Based on Machine Vision. Symmetry 2020, 12, 1158. https://doi.org/10.3390/sym12071158
Chen J, Jing L, Hong T, Liu H, Glowacz A. Research on a Sliding Detection Method for an Elevator Traction Wheel Based on Machine Vision. Symmetry. 2020; 12(7):1158. https://doi.org/10.3390/sym12071158
Chicago/Turabian StyleChen, Jiayan, Limeng Jing, Tao Hong, Hui Liu, and Adam Glowacz. 2020. "Research on a Sliding Detection Method for an Elevator Traction Wheel Based on Machine Vision" Symmetry 12, no. 7: 1158. https://doi.org/10.3390/sym12071158