Statistical Monitoring Of Image Data Using Multi-Channel Functional Principal Component Analysis

In recent years, some methods are proposed to analyze image data in statistical process control. These methods are mainly based on monitoring image data using statistical profile monitoring. In these methods, color images are converted to gray-scale images because of the simplicity of working. In this conversion, the color property of the images is generally removed, and the resulting profile does not reveal changes in the images. The main goal of this article is to develop a statistical approach based on multi-channel profiles for monitoring color image data. The proposed method applies a multi-channel functional principal component analysis to obtain a set of extracted features that can be effectively used to characterize process variations. These features can be used to construct an exponentially weighted moving average control chart. Numerous simulation studies are performed to evaluate the performance of the proposed method to detect shifts and change-point. Results for different fault sizes and smoothing constants indicate that the proposed method is capable of not only detecting shifts quickly but also estimating the change-point accurately. The results also illustrate the proper performance of the proposed approach in monitoring industrial cases to detect out-of-control conditions.