Multiple event identification and characterization by retrospective analysis of structured data streams

Abstract

The sensors installed in complex systems generate massive amounts of data, which contain rich information about a system’s operational status. This article proposes a retrospective analysis method for a historical data set, which simultaneously identifies when multiple events occur to the system and characterizes how they affect the multiple sensing signals. The problem formulation is motivated by the dictionary learning method and the solution is obtained by iteratively updating the event signatures and sequences using ADMM algorithms. A simulation study and a case study of the steel rolling process validate our approach. The supplementary materials including the appendices and the reproduction report are available online.

Additional information

Funding

This research is funded by the National Science Foundation Award ID 2019378.

Notes on contributors

Andi Wang

Andi Wang received a BS in statistics from Peking University in 2012, a PhD in industrial engineering from Hong Kong University of Science and Technology in 2016, an MS in computer science and engineering from Georgia Institute of Technology in 2021, and a PhD in industrial Engineering from Georgia Institute of Technology in 2021. He is now an assistant professor in the Ira A. Fulton Schools of Engineering, Arizona State University. Andi's research interests include smart manufacturing, data fusion and data-driven systems modeling, process monitoring, and root cause diagnostics. He is a member of Institute of Industrial and Systems Engineers (IISE) and Institute for Operations Research and the Management Sciences (INFORMS).

Tzyy-Shuh Chang

Tzyy-Shuh Chang received BS, MS, and PhD degrees in mechanical engineering from National Taiwan University (1987), the Ohio State University (1991), and the University of Michigan (1995), respectively. He co-founded OG Technologies, Inc., a Michigan corporation, and led the company to be the globally leading supplier and brand of advanced surface inspection equipment, an R&D100 awardee, for the steel industry. Since its inception, OG Technologies has established its business by way of advanced research and development and cooperative activities with the academia and metal industry, focusing on bringing the state-of-the-art technologies in sensing and data analytics to the millennium old industry.

Jianjun Shi

Jianjun Shi received BS and MS degrees in automation from the Beijing Institute of Technology in 1984 and 1987, respectively, and a PhD degree in mechanical engineering from the University of Michigan in 1992. Currently, Dr. Shi is the Carolyn J. Stewart Chair and Professor at the Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology. His research interests include the fusion of advanced statistical and domain knowledge to develop methodologies for modeling, monitoring, diagnosis, and control for complex manufacturing systems. Dr. Shi is a Fellow of the Institute of Industrial and Systems Engineers (IISE), a Fellow of American Society of Mechanical Engineers (ASME), a Fellow of the Institute for Operations Research and the Management Sciences (INFORMS), a Fellow of Society of Manufacturing Engineers (SME), an elected member of the International Statistics Institute, a life member of the American Statistics Association (ASA), an Academician of the International Academy for Quality (IAQ), and a member of National Academy of Engineers (NAE).