Publications

Google Scholar

Refereed Journal Publications

  1. Fang X., Zhou R., and Gebraeel N. (2015). An Adaptive Functional Regression-Based Prognostic Model for Applications with Missing Data. Reliability Engineering & System Safety, 133, 266-274.
  2. Fang X., Gebraeel N., and Paynabar K. (2017). Scalable Prognostic Models for Large-Scale Condition Monitoring Applications. IISE Transactions, 49(7), 698-710. (Feature article in the June 2017 issue of ISE Magazine)
  3. Fang X., Paynabar K., and Gebraeel N. (2017). Multistream Sensor Fusion-Based Prognostics Model for Systems with Single Failure Modes. Reliability Engineering & System Safety, 159, 322-331 (Finalist, Best Student Paper Award, INFORMS Workshop on Data Mining & Decision Analytics, 2017).
  4. Xia T., Xi L., Pan E., Fang X., Gebraeel N. (2017). Lease-Oriented Opportunistic Maintenance for Multi-Unit Leased Systems Under Product-Service Paradigm. Journal of Manufacturing Science and Engineering, 139(7), 071005.
  5. Fang X., Paynabar K., and Gebraeel N. (2019). Image-Based Prognostics Using Penalized Tensor Regression. Technometrics, 61(3), 369-384. (Winner of the SAS Data Mining Best Paper Award, Data Mining Section of INFORMS, 2016; Finalist of the QSR Best Refereed Paper Award, Quality, Statistics, and Reliability (QSR) Section of INFORMS, 2016)
  6. Xia T., Fang X., Gebraeel N., Xi L., and Pan E. (2019). Online Analytics Framework of Sensor-Driven Prognosis and Opportunistic Maintenance for Mass Customization. Journal of Manufacturing Science and Engineering, 141(5), 051011.
  7. Dong Y., Xia T., Fang X., Zhang Z., and Xi L. (2019). Prognostic and Health Management for Adaptive Manufacturing Systems with Online Sensors and Flexible Structures. Computers & Industrial Engineering, 133, 57-68.
  8. Fang X., Yan H., Gebraeel N., and Paynabar K. (2020). Multi-Sensor Prognostics Modeling for Applications with Highly Incomplete Signals. IISE Transactions, 53(5), 597-613. (Feature article in the April 2021 issue of ISE Magazine)
  9. Li N., Gebraeel N., Lei Y., Fang X., and Han T. (2021). Remaining Useful Life Prediction with Multi-Sensor Data Fusion Based on a Multivariate State-Space Model. Reliability Engineering & System Safety, 208, 107249.
  10. Dong Y., Xia T., Wang D., Fang X., and Xi L. (2021). Infrared Image Stream-Based Regressors for Contactless Machine Prognostics, Mechanical Engineering and Signal Processing, 154, 107592.
  11. Xia T., Zhang K., Sun B., Fang X., and Xi L. (2021). Integrated Remanufacturing and Opportunistic Maintenance Decision-Making for Leased Batch Production Lines. Journal of Manufacturing Science and Engineering, 143(8), 081003.
  12. Qian Q., Fang X., Xu J., and Li M. (2021). Multichannel Profile-Based Anomaly Detection and Its Application in the Monitoring of Basic Oxygen Furnace Steelmaking Processes. Journal of Manufacturing Systems, 61, 375-390.
  13. Lin F., Fang X., & Gao Z. (2022). Distributionally Robust Optimization: a review on theory and applications. Numerical Algebra, Control & Optimization, 12(1), 159.
  14. Jiang, Y., Xia, T., Wang, D., Fang, X., and Xi, L. (2022). Adversarial Regressive Domain Adaptation Framework for Infrared Thermography-based Unsupervised Remaining Useful Life Prediction, IEEE Transactions on Industrial Informatics, 18(10), 7219 – 7229
  15. Jiang, Y., Xia, T., Wang, D., Fang, X., and Xi, L. (2022). Spatiotemporal Denoising Wavelet Network for Infrared Thermography-based Machine Prognostics Integrating Ensemble Uncertainty, Mechanical Systems and Signal Processing, 173, 109014
  16. Jeong C. and Fang X. (2022). Two-Dimensional Variable Selection and Its Applications in the Diagnostics of Product Quality Defects. IISE Transactions,  54(7), 619-629. (Winner of the QCRE Best Student Paper Award, Quality Control & Reliability Engineering (QCRE) Division of IISE, 2020)
  17. Zhou, C. and Fang, X. (2023). A Convex Two-Dimensional Variable Selection Method for the Root Cause Diagnostics of Product Quality Defects, Reliability Engineering & System Safety, 229, 108827.
  18. Fang, X., Paynabar, K., and Gebraeel, N.(2022). A Supervised Dimension Reduction-Based Prognostics Model for Applications with Incomplete Signals, IISE Transactions, Under Revision.
  19. Jiang, Y., Xia, T., Fang, X., Wang, D., Pan, E., and Xi, L. (2022) Sparse Hierarchical Parallel Residual Networks Ensemble for Infrared Image Stream-Based Prognostics, IEEE Transactions on Industrial Informatics, Under Revision.
  20. Jeong, C. , Byon, E., He, F., and Fang, X. (2022). Tensor-Based Quality Fault Diagnostics Using Multi-Steam High-Dimensional Signals, IEEE Transactions on Automation Science and Engineering, Under Revision.
  21. Zhou, C. and Fang, X. (2022). A Supervised Tensor Dimension Reduction Method with Applications in Prognostics, INFORMS Journal on Data Science, Under Review. (Finalist, QSR Best Student Paper Award, Quality, Statistics, and Reliability (QSR) Section of INFORMS, 2022)
  22. Su, Y. and Fang, X. (2022). Deep Learning-Based Residual Lifetime Distribution Prediction for Assets with Multiple Failure Modes, Draft to be submitted.
  23. Zhou, C. , Su, Y. , and Fang, X. (2022). Federated Multilinear Principal Component Analysis with Applications in Prognostics, Draft to be submitted.
  24. Lin, F., Fang, S., Fang, X., Gao, Z., and Luo, J. (2022). Distributionally Robust Chance-Constrained Soft Quadratic Support Vector Machines with Second-Order Moment Information, Draft to be submitted.
  25. Su, Y. and Fang, X. (2022). Federated (Log)-Location-Scale Regression for System Prognostics, Draft to be submitted.
  26. Arabi, M. and Fang, X. (2022). A Federated Data Fusion-Based Prognostics Model for Applications with Incomplete Signals, Draft to be submitted.

Refereed Conference Proceedings

  1. Fang X., Paynabar K., and Gebraeel N. (2018). Real-Time Predictive Analytics Using Degradation Image Data. Reliability and Maintainability Symposium (RAMS), 2017 Annual, pp. 1-6. IEEE.
  2. Li, X. and Fang, X., 2021, June. Multistream Sensor Fusion-Based Prognostics Model for Systems Under Multiple Operational Conditions. In International Manufacturing Science and Engineering Conference (Vol. 85079, p. V002T09A003). American Society of Mechanical Engineers.