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

ABSTRACT

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.

KEYWORDS:

• Image-based process monitoring
• partial least squares discriminant analysis
• area Delaunay triangulation
• control chart

Additional information

Notes on contributors

Adel Alaeddini

Adel Alaeddini is an assistant professor of Mechanical Engineering at the University of Texas at San Antonio (UTSA). He obtained his Ph.D. in industrial and systems engineering from Wayne State University in 2011. He also did a post doc at University of Michigan, Ann Arbor. His main research interests include statistical learning in systems modeling and control and data analytics in health care and manufacturing. He has contributed to over 24 peer-reviewed publications in journals such as Production and Operations Management (POMS), IIE Transactions on Healthcare Systems Engineering, and Information Sciences.

Abed Motasemi

Abed Motasemi obtained his Ph.D. in mechanical engineering from the UTSA in 2016. He received his M.Sc. degree in mechanical engineering from Amir Kabir University, Tehran, Iran, and his B.S. degree in mechanical engineering from Tabriz University, Tabriz, Iran. His research interests are image-based process monitoring and high-dimensional data analysis. His work has appeared in journals such as Quality and Reliability Engineering International and Journal of Materials Engineering and Performance.

Syed Hasib Akhter Faruqui

Syed Hasib Akhter Faruqui is a Ph.D. student in mechanical engineering at UTSA. He also received his M.Sc. in mechanical engineering from UTSA in 2016. His research interests are probabilistic graphical models, scalable graph mining, and time series models.