A Sparse Bayesian Learning for Diagnosis of Nonstationary and Spatially Correlated Faults:References

Table of Links

• Abstract & Introduction
• Review of Related Work
• Proposed Research Methodology
• Numerical Case Studies
• Real-World Simulation Case Studies
• Conclusion
• References

REFERENCES

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received his B.S. degree in Industrial Engineering from the Hanyang University, Seoul, Korea, in 2015. He obtained his M.S. degree in Industrial and Systems Engineering at Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Korea, in 2017. He received a Ph.D. degree in Industrial and Systems Engineering from Virginia Tech, Blacksburg, VA, USA. His research interests include statistical learning and data analytics in smart manufacturing.

(Member, IEEE) received the B.S. and M.S. degrees in mechanical engineering from Harbin Institute of Technology, Harbin, China, in 1993 and 1995, respectively, and the Ph.D. degree from the Department of Industrial and System Engineering, University of Wisconsin–Madison, Madison, WI, USA, in 2004. He is currently a Professor with the Grado Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA, USA. His research interests include sensing and analytics for smart manufacturing, and modeling, synthesis, and diagnosis for large and complex manufacturing systems.