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

Research Interests:

  • Addressing analytical, computational, and scalability challenges
  • Development of statistical and optimization methodologies
  • Analyzing massive amounts of complex data structures
  • Real-time asset management and optimization

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Publications

A convex two-dimensional variable selection method for the root-cause diagnostics of product defects
Zhou, C., & Fang, X. (2023), RELIABILITY ENGINEERING & SYSTEM SAFETY, 229. https://doi.org/10.1016/j.ress.2022.108827
Sparse Hierarchical Parallel Residual Networks Ensemble for Infrared Image Stream-Based Remaining Useful Life Prediction
Jiang, Y., Xia, T., Fang, X., Wang, D., Pan, E., & Xi, L. (2023), IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 19(10), 10613–10623. https://doi.org/10.1109/TII.2022.3229493
Systems and methods for authenticating manufacturing Machines through an unobservable fingerprinting system
Koprov, P., Gadhwala, S., Walimbe, A., Fang, X., & Starly, B. (2023), Manufacturing Letters, 35, 1009–1018. https://doi.org/10.1016/j.mfglet.2023.08.051
Adversarial Regressive Domain Adaptation Approach for Infrared Thermography-Based Unsupervised Remaining Useful Life Prediction
Jiang, Y., Xia, T., Wang, D., Fang, X., & Xi, L. (2022), IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 18(10), 7219–7229. https://doi.org/10.1109/TII.2022.3154789
DISTRIBUTIONALLY ROBUST OPTIMIZATION: A REVIEW ON THEORY AND APPLICATIONS
Lin, F., Fang, X., & Gao, Z. (2022). [Review of , ]. NUMERICAL ALGEBRA CONTROL AND OPTIMIZATION, 12(1), 159–212. https://doi.org/10.3934/naco.2021057
Spatiotemporal denoising wavelet network for infrared thermography-based machine prognostics integrating ensemble uncertainty
Jiang, Y., Xia, T., Wang, D., Fang, X., & Xi, L. (2022), MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 173. https://doi.org/10.1016/j.ymssp.2022.109014
Two-dimensional variable selection and its applications in the diagnostics of product quality defects
Jeong, C., & Fang, X. (2022), IISE TRANSACTIONS, 54(7), 619–629. https://doi.org/10.1080/24725854.2021.1904524
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, 143(8). https://doi.org/10.1115/1.4049963
Multi-sensor prognostics modeling for applications with highly incomplete signals
Fang, X., Yan, H., Gebraeel, N., & Paynabar, K. (2021), IISE TRANSACTIONS, 53(5), 597–613. https://doi.org/10.1080/24725854.2020.1789779

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Xiaolei Fang