中国安全科学学报 ›› 2019, Vol. 29 ›› Issue (11): 122-128.doi: 10.16265/j.cnki.issn1003-3033.2019.11.020

• 安全工程技术科学 • 上一篇    下一篇

基于图像处理的电梯曳引轮轮槽磨损识别方法

谢小娟 工程师, 杨宁祥 高级工程师, 陈建勋, 林晓明 高级工程师   

  1. 广东省特种设备检测研究院 珠海检测院,广东 珠海 519002
  • 收稿日期:2019-08-08 修回日期:2019-10-15 发布日期:2020-10-30
  • 作者简介:谢小娟 (1988—),女,河北唐山人,硕士,工程师,主要从事特种设备安全检测方法、仪器等方面的研究。E-mail:rk1019@163.com。
  • 基金资助:
    广东省特种设备检测研究院科技项目(2018JD03)。

Wear recognition method for traction wheel groove of elevator based on image processing

XIE Xiaojuan, YANG Ningxiang, CHEN Jianxun, LIN Xiaoming   

  1. Zhuhai Branch, Guangdong Insitute of Special Equipment Inspection and Research, Zhuhai Guangdong 519002, China
  • Received:2019-08-08 Revised:2019-10-15 Published:2020-10-30

摘要: 为提高电梯曳引轮轮槽磨损的检测效率,提出一种基于图像处理技术的电梯曳引轮轮槽异常磨损的识别方法。首先提取曳引钢丝绳相对离散特征参数,以表征曳引轮轮槽的累积异常磨损量;然后采用改进的Canny算子对曳引钢丝绳的正、侧向图像进行去噪处理与边缘检测,再利用随机Hough变换和最小二乘法提取钢丝绳在正、侧向图像中的轮廓;最后根据钢丝绳在正、侧图像中所覆盖的像素数判断曳引轮轮槽的异常磨损程度。结果表明:基于图像处理技术的电梯曳引轮轮槽异常磨损识别方法相比于传统的机械测量法,能准确评估曳引轮轮槽的异常磨损状态,大幅节约检测时间和成本。

关键词: 曳引轮轮槽, 异常磨损, Canny算子, 随机Hough变换, 边缘检测

Abstract: In order to improve the inspection efficiency of the elevator traction wheel groove wear, a method based on image processing technology for identifying abnormal wear of elevator traction wheel groove was proposed. The relative discrete feature parameters of the traction wire ropes were extracted to characterize the cumulative abnormal wear of traction wheel groove, and the modified Canny operator was used to denoise and detect edges of the forward and lateral images of traction wire ropes. On this basis, the edges of ropes in the forward and lateral images were extracted by random Hough transform and least squares method. Finally, the abnormal wear degree of the traction wheel groove was calculated according to the number of pixels covered in images of tranction wire ropes. The results show that this proposed method can accurately evaluate the abnormal wear state of the traction wheel groove, and that compared with traditional measurement methods, this method can not only improve the inspection efficiency but also save the test cost.

Key words: traction wheel groove, abnormal wear, Canny operator, random Hough transform, edge detection

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