Research on Misalignment Detection of Paper Machine Clothing Based on Computer Vision

Abstract

 Paper machine clothing (PMC) is a specialized material essential to the paper industry, forming an integral part of paper-making equipment. Deviations in the net carpet, in particular, can lead to pleats along the edge or in the middle of the net carpet, making it challenging to spread and often resulting in broken pleats. These pleats contribute to increased paper-making costs. To address this issue of paper machine clothing misalignment, this study proposes a monitoring approach based on computer vision. Initially, image filtering and edge enhancement are applied to the video images obtained from the complex manufacturing environment of a paper mill. The Canny operator is then utilized for edge detection to obtain a binary image of the edges. Subsequently, Hough transform is employed to detect the edges of the net blanket based on the straight-line characteristics of paper machine clothing. Additionally, regions of interest (ROI) are established to enhance processing speed and reliability. If broken straight line segments are observed at the edge of the mesh blanket, these are combined using least squares fitting to form a complete straight line. Finally, deviation in the paper machine clothing is monitored by analyzing the slope value of the fitted edge straight line and its distance from the x-axis of the outer roller edge. Test results demonstrate that using the method proposed in this study, it is possible to determine both the angle of net blanket deviation and its distance from the rollers' edges. Through validation using laboratory and mill data, the algorithm presented in this study effectively reflects these parameters.