Multistage Manufacturing Processes: Innovations in Statistical Modeling and Inference
Modeling multistage manufacturing processes for fault detection and diagnosis in modern production systems has emerged as a cutting-edge research area at the interface of the engineering and statistical sciences. We give an overview of the developments in this area and describe some recent innovations in statistical modeling and inference associated with these developments.
Hsu’s research was partially supported by the Ministry of Science and Technology of Taiwan under grant MOST 105-2118-M-390-004. Ing’s research was supported by the Science Vanguard Research Program, Ministry of Science and Technology, Taiwan. Lai’s research was supported by National Science Foundation grant DMS-1407828. Yu’s research was partially supported by the Ministry of Science and Technology of Taiwan under grant MOST 105-2118-M-390-001.
- Djurdjanovic, D., & Ni, J. (2001). Stream of variation based analysis and synthesis of measurement schemes in multi-station machining systems. In Proceedings of the International Mechanical Engineering Congress and Exposition. New York.Google Scholar
- Grout, I. A. (2006). Integrated Circuit Test Engineering: Modern Techniques. New York: Springer.Google Scholar
- Ing, C. -K., & Lai, T. L. (2011). A stepwise regression method and consistent model selection for high-dimensional sparse linear models, Statistica Sinica, 21, 1473–1513.Google Scholar
- Ing, C. -K., Lai, T. L., Shen, M., Tsang, K. W., & Yu, S. -H. (2017). Multiple testing in regression models with applications to fault diagnosis in big data era. Technometrics. https://doi.org/10.1080/00401706.2016.1236755.
- Lai, T. L. (1995). Sequential changepoint detection in quality control and dynamical systems (with discussion and rejoinder). Journal of the Royal Statistical Society: Series B, 57, 613–658.Google Scholar
- Lai, T. L. (2000). Sequential multiple hypothesis testing and efficient fault detection-isolation in stochastic systems. IEEE Transactions on Information Theory, 46(2), 595–608.Google Scholar
- Lai, T. L. (2001). Sequential analysis: Some classical problems and new challenges. Statistica Sinica, 11, 303–408.Google Scholar
- Lai, T. L. (2004). Likelihood ratio identities and their applications to sequential analysis. Sequential Analysis, 23, 467–497.Google Scholar
- Lai, T. L., Shen, M., & Tsang, K. W. (2017). A new approach to high-dimensional process monitoring and diagnosis. Technical Report, Department of Statistics, Stanford University.Google Scholar
- Lai, T. L., & Tsang, K. W. (2017). Post-selection multiple testing and a new approach to test-based variable selection. Technical Report, Department of Statistics, Stanford University.Google Scholar
- Li, Y., & Tsung, F. (2009). False discovery rate-adjusted charting schemes for multistage process monitoring and fault identification. Technometrics, 51, 186–205.Google Scholar
- Lorden, G. (1971). Procedures for reacting to a change in distribution. The Annals of Mathematical Statistics, 1897–1908.Google Scholar
- Xie, Y., & Siegmund, D. (2013). Sequential multi-sensor change-point detection. The Annals of Statistics, 41, 670–692.Google Scholar