# Research on a Sliding Detection Method for an Elevator Traction Wheel Based on Machine Vision

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## 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

_{s}is the pixel size of the standard test block.

#### 4.2. Building the Experimental Platform

## 5. Test Result Analysis

#### 5.1. On-Site Testing

_{l}, and D

_{l}< 800 mm on the traction wheel and the wire rope. The markers are numbered in turn. In this way, the original collected image has only two white marks. When we use the detection method above to obtain the interval of the two markers, we need to identify the serial number on the markers by OCR to determine whether the two markers are the same as the two markers before the elevator runs. If the makers are the same, Formula (12) will be used to calculate the slippage, and if the markers are not the same, the distance between the mark in the image and the mark before the elevator ran should be added; then, the slip can be obtained by Formula (17).

#### 5.2. Error and Measurement Uncertainty Analysis

#### 5.2.1. Error Analysis

_{d}.

_{d}= 35.6 mm is not an abnormal point, and there is no gross error in the nine groups of experimental results.

#### 5.2.2. Measurement Uncertainty Analysis

_{0.95}(8) = 1.86. The inclusion factor k = 1.86, and the extension uncertainty U of the elevator slippage can be calculated by Formula (21):

_{c}= 1.86 × 0.37 mm = 0.688 ≈ 0.69 mm

_{R}is used to evaluate the uncertainty of the elevator slippage detection.

_{R}= (33.8 ± 0.69) mm, p = 0.95, and v = 8.

## 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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**MDPI and ACS Style**

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

**AMA Style**

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 Style**

Chen, 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