Supervised subgraph augmented non-negative matrix factorization for interpretable manufacturing time series data analytics

Abstract

Data analytics has been extensively used for manufacturing time series to reduce process variation and mitigate product defects. However, the majority of data analytics approaches are hard to understand for humans who do not have a data analysis background. Many manufacturing conditions, such as trouble shooting, need situation-dependent responses and are mainly performed by humans. Therefore, it is critical to discover insights from the time series and present those to a human operator in an interpretable format. We propose a novel Supervised Subgraph Augmented Non-negative Matrix Factorization (Super-SANMF) approach to represent and model manufacturing time series. We use a graph representation to approximate a human’s description of time series changing patterns and identify frequent subgraphs as common patterns. The appearances of the subgraphs in the time series are organized in a count matrix, in which each row corresponds to a time series and each column corresponds to a frequent subgraph. Super-SANMF then identifies groups of subgraphs as features that minimize the Kullback–Leibler divergence between measured and approximated matrices. The learned features can yield comparable prediction accuracy (normal or defective) in case studies, compared with the widely used basis expansion approaches (such as spline and wavelet), and are easy for humans to memorize and understand.

Keywords:

• Interpretable data analytics
• manufacturing time series
• subgraph augmented matrix factorization

Additional information

Funding

Hongyue Sun is partially supported by Sustainable Manufacturing and Advanced Robotics Technologies, Community of Excellence (SMART CoE) at University of Buffalo.

Notes on contributors

Hongyue Sun

Hongyue Sun received a B.E. degree in mechanical engineering and automation from the Beijing Institute of Technology, Beijing, China, in 2012, an M.S. degree in statistics, and a Ph.D. degree in industrial engineering from Virginia Tech, Blacksburg, VA, USA, in 2015 and 2017, respectively. He is an assistant professor with the Department of Industrial and Systems Engineering, University at Buffalo, Buffalo, NY, USA. His research interests are data analytics for advanced manufacturing processes and energy systems. He is a member of INFOMRS, IISE, IEEE and ASME.

Ran Jin

Ran Jin received his Ph.D. degree in Industrial Engineering from Georgia Tech (2011), his master’s degree in industrial engineering (2007) and in statistics (2009), both from the University of Michigan, and his bachelor’s degree in electronic engineering from Tsinghua University (2005). He is an assistant professor at the Grado Department of Industrial and Systems Engineering at Virginia Tech. His research interests are in engineering-driven data fusion for manufacturing system modeling and performance improvements, such as the integration of data mining methods and engineering domain knowledge for multistage system modeling and variation reduction, and sensing, modeling, and optimization based on spatial correlated responses. He is a member of INFORMS, IISE, ASME, TMS, SME and IEEE.

Yuan Luo

Yuan Luo is an assistant professor at the Department of Preventive Medicine, Division of Health & Biomedical Informatics (at Feinberg School of Medicine) with courtesy appointments in IEMS and EECS (both at McCormick School of Engineering). He earned his PhD degree from MIT EECS in 2015. His research interests include machine learning, natural language processing, time series analysis, computational genomics and big data analytics, with a focus on medical applications. He is a member of Association for the Advancement of Artificial Intelligence (AAAI), American Association for the Advancement of Science (AAAS) and American Medical Informatics Association (AMIA). He was also a member of the Student Editorial Board for Journal of the American Medical Informatics Association. ​