Distribution-free image monitoring with application to battery coating process

Abstract

This article presents a distribution-free image monitoring procedure for a manufacturing process, where a series of images are converted into a vector of two feature values extracted from singular value decomposition. Traditional image-based monitoring methods often make specific assumptions about marginal distributions and spatio-temporal dependence structures, which are often violated in real-world scenarios such as battery coating processes. To overcome this issue, we propose a distribution-free image monitoring procedure that detects a shift in the mean matrix of monitored images. Our method involves performing singular value decomposition of each image matrix in two ways to obtain two values, which are then combined into a bivariate vector. The bivariate vectors are monitored using a distribution-free multivariate CUSUM procedure, for which we determine control limits analytically, enabling convenient and easy implementation of the monitoring procedure. We demonstrate the effectiveness of our proposed procedure, as measured by average run lengths, using various simulated data and a real-data example from a battery coating process.