Yunhao Liu, Ph.D. - US grants

This project aims to develop non-intrusive and universal vibration sensing schemes that can detect the abnormal vibrations of a running machine. Towards this goal, the researchers propose a system that first uses the backscatter signals in commercial off the shelf RFID systems to accurately measure machine vibrations, and then uses machine learning and signal processing techniques to detect abnormal machine vibration patterns so that machine operators can be alerted to take actions before the machine fails. This project represents an emerging space driving new CPS and Internet of Things concepts for machinery safety. It can be used for the prognostic monitoring of not only indoor machines, but also outdoor appliances and civil infrastructures, such as drilling system monitoring, pumping system monitoring, pipeline system monitoring, and bridge monitoring. The proposed system is expected to impact manufacturing and economy. This project will bridge the communities between Computer Science and Mechanical Engineering; and foster interaction and communication among them. It will also facilitate the effort of the researchers on attracting and mentoring undergraduate students and underrepresented graduate students in research. Furthermore, the researchers will integrate the research results from this project into both undergraduate and graduate curricula.

This project has two key technical objectives: to develop vibration measurement schemes using RFID systems and to develop abnormal vibration pattern recognition schemes based on the measured vibration signals. For vibration sensing, the basic idea is to measure the machine vibrations through random and low-frequency readings of the tag using the RFID reader, where each reading is viewed as one sampling of the vibration. For abnormal vibration pattern detection, the basic idea is to build base line models based on the measured vibration readings and then classify real time vibration readings of a running machine as being either normal or abnormal. The proposed system would have several advantages over prior art in machine health monitoring , e.g., nonintrusive, inexpensive, accurate, and easily deployable including in non-line-of-sight scenarios.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

The requirement on timely processing and analysis of huge volumes of data generated by various applications in our daily lives has driven an steady but urgent need to speed up the computing power. However, due to the physical limits in transistor scaling and the cross-core interference in multicore systems, the computer processor design for large-scale parallel computing is facing its limits. A new network-level parallel computing architecture and innovative performance optimization algorithms developed in this project can free the speedup of computing power and completely break the barriers in computing processor hardware design. The new technologies resulted from this project can be widely used in many parallel computing related applications for timely analysis of big data and secure data storage. Moreover, by integrating the technological advances resulting from this project into the undergraduate/graduate curricula and outreach activities, this project has significant impacts on the training of a highly-skilled and diverse workforce for high-performance computing.

The goal of this project is to achieve a theoretically-optimal speedup of computing power by leveraging the computing capability of the edge and cloud computing resources in the computing network, so as to enable efficient, scalable and secure parallel computing at the network level. More specifically, (i) this project develops new network-level parallel computing architectures and innovative performance optimization algorithms that can achieve a linear speedup of computing power by leveraging the computing capability of the edge nodes and cloud computing servers in the network; (ii) this project breaks new ground in developing secure parallel cloud computing and data storage schemes. The new techniques make it possible to implement efficient network-level parallel computing schemes without compromising the security requirements, and hence pave the path for the wide adoption of network-level parallel computing in future big-data applications.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.