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

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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., 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