Xiaolei Fang

Assistant Professor

 
My research interests lie in the field of industrial data analytics for High-Dimensional and Big Data applications in the energy, manufacturing, and service sectors. Specifically, I focus on addressing analytical, computational, and scalability challenges associated with the development of statistical and optimization methodologies for analyzing massive amounts of complex data structures for real-time asset management and optimization.

Methodologies

  • Data Science
  • Machine Learning
  • Artificial Intelligence

Applications:

  • Condition Monitoring
  • Anomalies Detection
  • Fault Root-Cause Diagnostics
  • Degradation Modeling and Failure Time Prognostics
  • System Performance Assessment, Optimization, Decision-making, and Control

Personal Website

 

Research Interests

Xiaolei Fang’s research focuses on the field of industrial data analytics for High-Dimensional and Big Data applications in the energy, manufacturing, and service sectors. Specifically, he focuses on addressing analytical, computational, and scalability challenges associated with the development of statistical and optimization methodologies for analyzing massive amounts of complex data structures for real-time asset management and optimization.
 

Education

DegreeProgramSchoolYear
Ph.D.Industrial EngineeringGeorgia Institute of Technology2014-2018
MSStatisticsGeorgia Institute of Technology2014-2016
BSMechanical EngineeringUniversity of Science and Technology Beijing2004-2008
 

Honors and Awards

  • 2019 | Winner, Sigma Xi Best Ph.D. Thesis Award, Georgia Institute of Technology
  • 2018 | Winner, Alice and John Jarvis Ph.D. Student Research Award, H. Milton Stewart School of Industrial & Systems Engineering, Georgia Institute of Technology
  • 2017 | Feature Article in ISE Magazine
  • 2016 | Finalist, QSR Best Refereed Paper Award, INFORMS
  • 2016 | Winner, SAS Data Mining Best Paper Award, INFORMS

 

Discover more about Xiaolei Fang

 

Publications

Infrared image stream based regressors for contactless machine prognostics
Dong, Y., Xia, T., Wang, D., Fang, X., & Xi, L. (2021), MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 154. https://doi.org/10.1016/j.ymssp.2020.107592
Integrated Remanufacturing and Opportunistic Maintenance Decision-Making for Leased Batch Production Lines
Xia, T., Zhang, K., Sun, B., Fang, X., & Xi, L. (2021), JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING-TRANSACTIONS OF THE ASME. https://doi.org/10.1115/1.4049963
Remaining useful life prediction based on a multi-sensor data fusion model
Li, N., Gebraeel, N., Lei, Y., Fang, X., Cai, X., & Yan, T. (2021), RELIABILITY ENGINEERING & SYSTEM SAFETY, 208. https://doi.org/10.1016/j.ress.2020.107249
Two-dimensional variable selection and its applications in the diagnostics of product quality defects
Jeong, C., & Fang, X. (2021), IISE TRANSACTIONS. https://doi.org/10.1080/24725854.2021.1904524
Multi-sensor prognostics modeling for applications with highly incomplete signals
Fang, X., Yan, H., Gebraeel, N., & Paynabar, K. (2020), IISE TRANSACTIONS. https://doi.org/10.1080/24725854.2020.1789779
Image-Based Prognostics Using Penalized Tensor Regression
Fang, X., Paynabar, K., & Gebraeel, N. (2019), TECHNOMETRICS, 61(3), 369–384. https://doi.org/10.1080/00401706.2018.1527727
Online Analytics Framework of Sensor-Driven Prognosis and Opportunistic Maintenance for Mass Customization
Xia, T., Fang, X., Gebraeel, N., Xi, L., & Pan, E. (2019), JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING-TRANSACTIONS OF THE ASME, 141(5). https://doi.org/10.1115/1.4043255
Prognostic and health management for adaptive manufacturing systems with online sensors and flexible structures
Dong, Y., Xia, T., Fang, X., Zhang, Z., & Xi, L. (2019), COMPUTERS & INDUSTRIAL ENGINEERING, 133, 57–68. https://doi.org/10.1016/j.cie.2019.04.051

View all publications via NC State Libraries

Xiaolei Fang