Wear Detection System for Elevator Traction Sheave

doi:10.1007/s12204-022-2433-6

Abstract

Abstract: In order to achieve accurate non-contact measurement of the mechanical wear of the elevator traction sheave grooves, a wear detection system based on machine vision was developed. The industrial camera was fixed through a special fixture, and the images were collected by aligning each groove. In this paper, target groove is extracted based on normalized correlation coefficient matching. Corner points are extracted to describe the contour of the traction wheel groove. The inflection point of the wheel groove boundary is determined by straight boundary fitting, and the amount of rope groove wear is calculated by geometric knowledge. Based on the physical model structure, a mathematical model is established to eliminate the unavoidable occlusion error. The experimental results show that this system can carry out an accurate quantitative analysis and realize an accurate measurement of the wear of the traction sheave rope groove with convenience and accuracy.

Key words: traction sheave, target groove, inflection point, boundary fitting, occlusion compensation

CLC Number:

TP274
TN29

LIU Shixing∗ (刘士兴), MA Dengke (马登科), LIU Guangzhu (刘光柱). Wear Detection System for Elevator Traction Sheave[J]. J Shanghai Jiaotong Univ Sci, 2022, 27(5): 706-714.

References 20

[1]	WANG H, ZOU H, CHEN D, et al. Analysis of the inspection method for wheel groove wear of elevator traction sheaves [J]. Technology and Market, 2017, 24(11): 158 (in Chinese).
[2]	NAKAZAWA D, WATANABE S, FUKUI D, et al. Elevator rope tension analysis with uneven groove wear of sheave [J]. Journal of Physics: Conference Series, 2018, 1048: 012006
[3]	General Administration of Qualify, Inspection and Quarantine of the People’s Republic of China. Specification for discard of the main parts of lifts GB/T 31821—2015 [S]. Beijing: Standards Press of China, 2015 (in Chinese)．
[4]	WU Z L, CHEN J X, LIN X M. Inspection method and research on the traction wheel groove wear of elevators [J]. China Plant Engineering, 2019, 35(9): 115-116 (in Chinese).
[5]	LI J B, LI W P. Development of a measuring device for the amount of rope groove wear on elevator traction wheels [J]. Hoisting and Conveying Machinery, 2017(1): 71-74 (in Chinese).
[6]	CHEN J X, WU Z L, LIN X M. Research on the noncontact detection method for the traction wheel groove of elevators [J]. Safety Technology of Special Equipment, 2019(5): 45-47 (in Chinese).
[7]	XIE X, YANG N, CHEN J, et al. Wear recognition method for traction wheel groove of elevator based on image processing [J]. China Safety Science Journal, 2019, 29(11): 122-128 (in Chinese).
[8]	DOGRU A, BOUARFA S, ARIZAR R, et al. Using convolutional neural networks to automate aircraft maintenance visual inspection [EB/OL]. (2020-11-20)
[9]	RAPINSKI J, SMIEJA M. ZigBee ranging using phase shift measurements [J]. Journal of Navigation, 2015, 68(4): 665-677.
[10]	YAN Y J, ASSAAD M, ZABALZA J, et al. Low cost structured-light based 3D surface reconstruction [J]. International Journal on Smart Sensing and Intelligent Systems, 2019, 12(1): 1-11.
[11]	YANG Y, TANG D, WANG D S, et al. Multi-camera visual SLAM for off-road navigation [J]. Robotics and Autonomous Systems, 2020, 128: 103505.
[12]	CHENG L, WU G P. Obstacles detection and depth estimation from monocular vision for inspection robot of high voltage transmission line [J]. Cluster Computing, 2019, 22(2): 2611-2627. [13] MAIRESSE J, MOYAL P. Editorial introduction to the special issue on stochastic matching models, matching queues and applications [J]. Queueing Systems, 2020, 96(3/4): 357-358.
[14]	WANG X Z, KEALY A, LI W C, et al. Toward autonomous UAV localization via aerial image registration [J]. Electronics, 2021, 10(4): 435.
[15]	CHI J N, ERAMIAN M. Enhancing textural differences using wavelet-based texture characteristics morphological component analysis: A preprocessing method for improving image segmentation [J]. Computer Vision and Image Understanding, 2017, 158: 49- 61.
[16]	NADEEM M, HUSSAIN A, MUNIR A, et al. Removal of random valued impulse noise from grayscale images using quadrant based spatially adaptive fuzzy filter [J]. Signal Processing, 2020, 169: 107403.
[17]	JI X G, ZHANG X X, HU H T. Point cloud segmentation for complex microsurfaces based on feature line fitting [J]. Multimedia Tools and Applications, 2021, 80(3): 4553-4578.
[18]	WANG Y, HUANG S Y, DENG Y Q, et al. Model test on the effect of hillside on lightning shielding performance of ±1 100 kV transmission lines [J]. International Journal of Electrical Power & Energy Systems, 2021, 129: 106734.
[19]	WANG S H, ZHANG X, ZHANG G W, et al. Optimization of quantum-dot light source and detection of the simulants of chemical warfare agent [J]. Optical Materials, 2021, 114: 110935.
[20]	ZENG S T, CAO Y C, LIN Q, et al. Deep learning SPECT lung perfusion image classification method based on attention mechanism [J]. Journal of Physics: Conference Series, 2021, 1748(4): 042050.