Title: IPCA-SVM based real-time wrinkling detection approaches for strip steel production process

Authors: Tong Zhao; Xiong Chen; Lirong Yang

Addresses: Intelligent Control Research Lab, Department of Electronic Engineering, Fudan University, Shanghai 200433, China ' Intelligent Control Research Lab, Department of Electronic Engineering, Fudan University, Shanghai 200433, China ' Intelligent Control Research Lab, Department of Electronic Engineering, Fudan University, Shanghai 200433, China

Abstract: Strip steel wrinkling is one of the common problems in strip steel production line. The wrinkling phenomenon has a serious impact on the quality of the products, resulting in product waste, and even leading to the entire production line downtime. The key point to solve the problem is real-time detection for strip wrinkling while making an early warning. This paper proposes an IPCA-SVM based online wrinkling detection approach. IPCA is used to compress each frame image from industrial camera and extract effective features from frame images. The projection coefficients of each frame image on the principal components are the inputs to SVM model for classification. Experiment results show that the proposed method is valid for real-time wrinkling detection for strip steel production process.

Keywords: strip wrinkling; IPCA-SVM; real-time detection.

DOI: 10.1504/IJWMC.2019.099021

International Journal of Wireless and Mobile Computing, 2019 Vol.16 No.2, pp.160 - 165

Received: 13 Jan 2018
Accepted: 30 Sep 2018
Published online: 12 Apr 2019 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article