Annals of Operations Research

, Volume 263, Issue 1–2, pp 191–207 | Cite as

A distance-based control chart for monitoring multivariate processes using support vector machines

  • Shuguang He
  • Wei Jiang
  • Houtao Deng
Data Mining and Analytics


Traditional control charts assume a baseline parametric model, against which new observations are compared in order to identify significant departures from the baseline model. To monitor a process without a baseline model, real-time contrasts (RTC) control charts were recently proposed to monitor classification errors when seperarting new observations from limited phase I data using a binary classifier. In contrast to the RTC framework, the distance between an in-control dataset and a dataset of new observations can also be used to measure the shift of the process. In this paper, we propose a distance-based multivariate process control chart using support vector machines (SVM), referred to as D-SVM chart. The SVM classifier provides a continuous score or distance from the boundary for each observation and allows smaller sample sizes than the previously random forest based RTC charts. An extensive experimental study shows that the RTC charts with the SVM scores are more efficient than those with the random forest for detecting changes in high-dimensional processes and/or non-normal processes. A real-life example from a mobile phone assembly process is also considered.


Average run length Classification High-dimensional processes Statistical process control Support vector machine 



The authors would like to thank the two anonymous referees for their helpful comments. Shuguang He’s research was supported by the National Natural Science Foundation of China #71472132 and #71532008; Wei Jiang’s research was supported by Program of Shanghai Subject Chief Scientist #15XD1502000 and the National Natural Science Foundation of China #71531010, #71172131, and #71325003.


  1. Amiri, A., & Allahyari, S. (2012). Change point estimation methods for control chart postsignal diagnostics: A literature review. Quality and Reliability Engineering International, 28(7), 673–685.CrossRefGoogle Scholar
  2. Chinnam, R. (2002). Support vector machines for recognizing shifts in correlated and other manufacturing processes. International Journal of Production Research, 40(17), 4449–4466.CrossRefGoogle Scholar
  3. Cook, D., & Chiu, C. (1998). Using radial basis function neural networks to recognize shifts in correlated manufacturing process parameters. IIE Transactions, 30(3), 227–234.Google Scholar
  4. Crosier, R. B. (1988). Multivariate generalizations of cumulative sum quality-control schemes. Technometrics, 30(3), 291–303.CrossRefGoogle Scholar
  5. Deng, H., Runger, G., & Tuv, E. (2012). System monitoring with real-time contrasts. Journal of Quality Technology, 44(1), 9–27.CrossRefGoogle Scholar
  6. Grandvalet, Y., Mariethoz, J., & Bengio, S. (2005). A probabilistic interpretation of SVMs with an application to unbalanced classification semi-parametric classification. In Advances in Neural Information Processing Systems 15 (Vol. 15). IDIAP-RR 05-26.Google Scholar
  7. Guh, R.-S., & Shiue, Y.-R. (2008). An effective application of decision tree learning for on-line detection of mean shifts in multivariate control charts. Computers and Industrial Engineering, 55(2), 475–493.CrossRefGoogle Scholar
  8. Hawkins, D. M., & Qiu, P. (2003). The changepoint model for statistical process control. Journal of Quality Technology, 35(4), 355–366.CrossRefGoogle Scholar
  9. Hawkins, D. M., & Zamba, K. D. (2005). Statistical process control for shifts in mean or variance using a changepoint formulation. Technometrics, 47(2), 164–173.CrossRefGoogle Scholar
  10. He, S.-G., He, Z., & Wang, G. A. (2013). Online monitoring and fault identification of mean shifts in bivariate processes using decision tree learning techniques. Journal of Intelligent Manufacturing, 24, 25–34.CrossRefGoogle Scholar
  11. Hotelling, H. H. (1947). Multivariate quality control. In C. Eisenhart, M. W. Hastay, & W. A. Wallis (Eds.), Techniques of statistical analysis (pp. 111–184). New York, NY: McGraw-Hill Professional.Google Scholar
  12. Hou, T. T., Liu, W., & Lin, L. (2003). Intelligent remote monitoring and diagnosis of manufacturing processes using an integrated approach of neural networks and rough sets. Journal of Intelligent Manufacturing, 14(2), 239–253.CrossRefGoogle Scholar
  13. Hu, J., Runger, G., & Tuv, E. (2007). Tuned artificial contrasts to detect signals. International Journal of Production Research, 45(23), 5527–5534.CrossRefGoogle Scholar
  14. Hwang, W., & Lee, J. (2015). Shifting artificial data to detect system failures. International Transactions in Operational Research, 22(2), 363–378.CrossRefGoogle Scholar
  15. Hwang, W., Runger, G., & Tuv, E. (2007). Multivariate statistical process control with artificial contrasts. IIE Transactions, 2(39), 659–669.CrossRefGoogle Scholar
  16. Jemwa, G. T., & Aldrich, C. (2005). Improving process operations using support vector machines and decision trees. American Institute of Chemical Engineers, 51(2), 526–543.CrossRefGoogle Scholar
  17. Khandoker, A. H., Lai, D. T. H., Begg, R. K., & Palaniswami, M. (2007). Wavelet-based feature extraction for support vector machines for screening balance impairments in the elderly. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 15(4), 587–597.CrossRefGoogle Scholar
  18. Lowry, C. A., Woodall, W. H., Champ, C. W., & Rigdon, S. E. (1992). A multivariate exponentially weighted moving average control chart. Technometrics, 34(1), 46.CrossRefGoogle Scholar
  19. Maboudou-Tchao, E. M., & Hawkins, D. M. (2011). Self-starting multivariate control charts for location and scale. Journal of Quality Technology, 43(2), 113–126.CrossRefGoogle Scholar
  20. Osuna, E., Freund, R., & Girosi, F. (1997). Training support vector machines: An application to face detection. In IEEE conference on computer vision and pattern recognition, pp. 130–136.Google Scholar
  21. Poursaeidi, M. H., & Kundakcioglu, O. E. (2014). Robust support vector machines for multiple instance learning. Annals of Operations Research, 216(1), 205–227.CrossRefGoogle Scholar
  22. Platt, J. C. (1999). Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. In A. J. Smola, P. Bartlett, B. Scholkopf, & D. Schuurmans (Eds.), Advances in large margin classifiers. Cambridge: MIT Press.Google Scholar
  23. Ross, G. J., & Adams, Niall M. (2012). Two nonparametric control charts for detecting arbitrary distribution changes. Journal of Quality Technology, 44(2), 102–116.CrossRefGoogle Scholar
  24. Scholkopf, B., Smola, A. J., Williamson, R. C., & Bartlett, P. L. (2000). New support vector algorithms. Neural Computation, 12, 1207–1245.CrossRefGoogle Scholar
  25. Sollich, P. (2000). Probabilistic methods for support vector machines. In S. A. Solla, T. K. Leen, & K. R. Muller (Eds.), Advances in neural information processing systems (pp. 349–355). Cambridge: MIT Press.Google Scholar
  26. Sukchotrat, T., Kim, S. B., & Tsung, F. (2010). One-class classification-based control charts for multivariate process monitoring. IIE Transactions, 42(2), 107–120.CrossRefGoogle Scholar
  27. Sullivan, J. H., & Woodall, W. H. (2000). Change-point detection of mean vector or covariance matrix shifts using multivariate individual observations. IIE Transactions, 32(6), 537–549.Google Scholar
  28. Sun, R., & Tsung, F. (2003). A kernel-distance-based multivariate control chart using support vector methods. International Journal of Production Research, 41(13), 2975–2989. doi: 10.1080/1352816031000075224.CrossRefGoogle Scholar
  29. Suykens, J. A. K., & Vandewalle, J. (1999). Least squares support vector machine classifiers. Neural Processing Letters, 9, 293–300.CrossRefGoogle Scholar
  30. Vapnik, V. N. (1998). Statistical learning theory. New York, NY: Springer.Google Scholar
  31. Wang, S., Jiang, W., & Tsui, K. L. (2010). Adjusted support vector machines based on a new loss function. Annals of Operations Research, 174(1), 83–101.CrossRefGoogle Scholar
  32. Yu, J. B., & Xi, L. F. (2009). A neural network ensemble-based model for on-line monitoring and diagnosis of out-of-control signals in multivariate manufacturing processes. Expert Systems with Applications, 36(1), 909–921.CrossRefGoogle Scholar
  33. Zamba, K. D., & Hawkins, D. M. (2006). A multivariate change-point model for statistical process control. Technometrics, 48(4), 539–549. doi: 10.1198/004017006000000291.CrossRefGoogle Scholar
  34. Zhang, Y., Chi, Z., Liu, X., & Wang, X. (2007). A novel fuzzy compensation multi-class support vector machine. Applied Intelligence, 27(1), 21–28.CrossRefGoogle Scholar
  35. Zou, C., Ning, X., & Tsung, F. (2012). LASSO-based multivariate linear profile monitoring. Annals of Operations Research, 192(1), 3–19.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.College of Management and EconomicsTianjin UniversityTianjinChina
  2. 2.Antai College of Economics and ManagementShanghai Jiao Tong UniversityShanghaiChina
  3. 3.InstacartSan FranciscoUSA

Personalised recommendations