Xiaolei Fang

Assistant Professor

 
Xiaolei Fang‘s research interest lies in the field of industrial data analytics for high-dimensional and big data applications in the energy, manufacturing, and service sectors.

Methodologies:

  • Data science
  • Machine learning

Applications:

  • System performance assessment and optimization
  • System anomalies detection
  • Fault root causes diagnostics
  • Remaining useful lifetime prediction
  • Decision-making and control

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Publications

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., & 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. (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