Bio

Xiaolei Fang‘s research interests lie in 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.

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

Publications

• Deep Complex Wavelet Denoising Network for Interpretable Fault Diagnosis of Industrial Robots With Noise Interference and Imbalanced Data , IEEE Transactions on Instrumentation and Measurement (2025)
• Deep Learning-Based Residual Useful Lifetime Prediction for Assets With Uncertain Failure Modes , Journal of Computing and Information Science in Engineering (2025)
• Enhancing Data Privacy in Human Factors Studies with Federated Learning , Human Factors The Journal of the Human Factors and Ergonomics Society (2025)
• A distributionally robust chance-constrained kernel-free quadratic surface support vector machine , European Journal of Operational Research (2024)
• A federated data fusion-based prognostic model for applications with multi-stream incomplete signals , IISE Transactions (2024)
• Distributionally robust chance-constrained kernel-based support vector machine , Computers & Operations Research (2024)
• IISE PG&E Energy Analytics Challenge 2024: Forecasting day-ahead electricity prices , IISE Transactions (2024)
• Image-based remaining useful life prediction through adaptation from simulation to experimental domain , Reliability Engineering & System Safety (2024)
• Learning Undergraduate Data Science Through a Mobile Device and Full Body Movements , TechTrends (2024)
• Machine identity authentication via unobservable fingerprinting signature: A functional data analysis approach for MQTT 5.0 protocol , Journal of Manufacturing Systems (2024)

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