The Internet of Things (IoT) technology enables the collection and sharing of relevant data across a wide range of devices. This capability, combined with real-time decision-making, creates unprecedented opportunities for system modeling, monitoring, prognosis, and decision-making. In this talk, new data analytics techniques tailored for industrial IoT systems will be introduced, including modeling and prognosis of condition monitoring signals using multivariate Gaussian convolution processes and a hidden Markov model with deep emission network. The advantageous features of the developed methods are demonstrated through numerical studies and real-world case studies. Thoughts on potential research and education opportunities exploiting the ever-growing data-rich engineering environment will be shared as well.
Shiyu Zhou is the David H. Gustafson Chair and Vilas Distinguished Achievement Professor of the Department of Industrial and Systems Engineering at the University of Wisconsin-Madison. His research focuses on data-driven modeling, monitoring, diagnosis, and prognosis for engineering systems with particular emphasis on manufacturing and after-sales service systems. He has established methods for modeling, analysis, and control of Internet-of-Things (IoT) enabled smart and connected systems, variation modeling, analysis, and reduction for complex manufacturing processes, and process control methodologies for emerging nano-manufacturing processes. He is a recipient of CAREER Award from the National Science Foundation and multiple Best Paper Awards. He is a fellow of IISE, ASME, and SME.