A spatiotemporal outlier detection method based on partial least squares discriminant analysis and area Delaunay triangulation for image-based process monitoring

Over the past two decades, statistical process control has evolved from monitoring individual data points to linear profiles to image data. Image sensors are now being deployed in complex systems at increasing rates due to the rich information they can provide. As a result, image data play an important role in process monitoring in different application domains ranging from manufacturing to service systems. Many of the existing process monitoring methods fail to take full advantage of the image data due to the data's complex nature in both the spatial and temporal domains. This article proposes a spatiotemporal outlier detection method based on the partial least squares discriminant analysis and a control statistic based on the area Delaunay triangulation of the squared prediction errors to improve the performance of an image-based monitoring scheme. First, the discriminant analysis of the partial least squares is used to efficiently extract the most important features from the high-dimensional image data to identify the benchmark images of the products and obtain the pixel value errors. Next, the squared errors resulting from the previous step are connected using a Delaunay triangulation to form a surface, the area of which is used as the control statistic for the purpose of outlier detection. A real case study at a paper product manufacturing company is used to compare the performance of the proposed method in detecting different types of outliers with some of the existing methods and demonstrate the merit of the proposed method.