Monitoring for changes in the nature of stochastic textured surfaces

We propose an approach for monitoring general global changes in the nature of stochastic textured surfaces using streams of high-dimensional images or related profile data. Stochastic textured surfaces are fundamentally different than the profiles and images that are the focus of most prior profile monitoring works. We represent normal in-control behavior by using supervised learning algorithms to implicitly characterize the joint distribution of the stochastic textured surface pixels. Based on this characterization, we develop a control chart monitoring statistic using likelihood-ratio principles to quantify and detect changes in the stochastic nature of the surfaces, relative to the in-control surfaces. Unlike methods that look for changes in specific predefined features, our approach can detect very general changes in the nature of the textured surfaces. We demonstrate the implementation and effectiveness of the approach with a real textile example and a simulation example.