Skip to main content
Log in

Process disturbance identification through integration of spatiotemporal ICA and CART approach

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Many studies have been conducted over the past several years evaluating the integrated use of statistical process control (SPC) and engineering process control (EPC). The majority of these studies reported that combining SPC with EPC outperforms the use of only SPC or EPC. Basically, the former aims to rapidly detect assignable causes and time points for abnormalities that take place during process; and the latter is a method in which input variables are adjusted against process outputs through a feedback control mechanism. Although combining SPC with EPC can effectively detect time points when abnormalities occur during process, their combination can also cause an increased occurrence of false alarms when autocorrelation is present in the process. In this study, to increase the accuracy of process disturbance identification, we propose the integration of spatiotemporal independent component analysis (stICA) with the classification and regression tree (CART) approach to improve our capability to identify process disturbances and recognize shifts in the correlated process parameters. The integration of the CART methodology results in the development of decision rules that can provide valuable information related to the impact of variation in process variable values. These decision rules can provide an increased understanding of process behavior and useful information for process control. For comparison, the integration of traditional principle component analysis (PCA) with CART (called PCA-CART), ICA with CART (called ICA-CART) and cumulative sum chart approaches were applied to evaluate the identification capability of the proposed approach. As the results reveal, the proposed approach is more effective for monitoring correlated process.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Adams BM, Tseng LT (1998) Robustness of forecast-based monitoring schemes. J Qual Technol 30:328–329

    Google Scholar 

  2. Albazzaz H, Wang XZ (2007) Introduction of dynamics to an approach for batch process monitoring using independent component analysis. Chem Eng Commun 194:218–233

    Article  Google Scholar 

  3. Alwan LC, Roberts HV (1988) Time series modeling for statistical process control. J Bus Econ Stat 6:87–95

    Article  Google Scholar 

  4. Breiman L, Friedman JH, Olshen RA, Stone CJ (1984) Classification and regression trees. Wadsworth International Group, Belmont

    MATH  Google Scholar 

  5. Box GEP, Coleman DE, Baxley RV (1997) A comparison of statistical process control and engineering process control. J Qual Technol 29:128–130

    Google Scholar 

  6. Box GEP, Jenkins GM, Reinsel GC (1992) Time series analysis, forecasting, and control, 3rd edn. Prentice-Hall, Englewood Cliffs

    Google Scholar 

  7. Box GEP, Luceno A (1997) Statistical control by monitoring and feedback adjustment. Wiley, New York

    MATH  Google Scholar 

  8. Box GEP, Kramer T (1992) Statistical process monitoring and feedback adjustment—a discussion. Technometrics 34:251–285

    Article  MathSciNet  Google Scholar 

  9. Calhoun VD, Adali T, Pearlson GD, Pekar JJ (2001) Spatial and temporal independent component analysis of functional MRI data containing a pair of task-related waveforms. Hum Brain Mapp 13:43–53

    Article  Google Scholar 

  10. Peng C, Qian X, Ye D (2007) Electrogastrogram extraction using independent component analysis with references. Neural Comput Appl 16(6):581–587

    Article  Google Scholar 

  11. Cichocki A, Amari SI (2002) Adaptive blind signal and image processing: learning algorithms and applications. Wiley, New York

    Book  Google Scholar 

  12. Comon P (1994) Independent component analysis: a new concept? Signal Process 36:287–314

    Article  MATH  Google Scholar 

  13. David V, Sanchez A (2002) Frontiers of research in BSS/ICA. Neruocomputing 49:7–23

    Article  MATH  Google Scholar 

  14. Faltin FW, Hahn GJ, Tucker WT, Vander Wiei SA (1993) Algorithmic statistical process control: some practical observations. Int Stat Rev 61:67–80

    Article  Google Scholar 

  15. Ge ZQ, Song ZH (2007) Process monitoring based on independent component analysis-principal component analysis (ICA-PCA) and similarity factors. Ind Eng Chem Res 46:2054–2063

    Article  Google Scholar 

  16. Hyvärinen A, Oja E (2000) Independent component analysis: algorithms and applications. Neural Netw 13:411–430

    Article  Google Scholar 

  17. Hyvärinen A (1999) Fast and robust fixed-point algorithms for independent component analysis. IEEE Trans Neural Netw 10:626–634

    Google Scholar 

  18. Hyvarinen A (1998) Independent component analysis in the presence of Gaussian noise by maximizing joint likelihood. Neurocomputing 22:49–67

    Article  Google Scholar 

  19. Hyvarinen A (1999) The fixed-point algorithm and maximum likelihood estimation for independent component analysis. Neural Process Lett 10:1–5

    Article  Google Scholar 

  20. Hyvarinen A, Karhunen J, Oja E (2001) Independent component analysis. Wiley, New York

    Book  Google Scholar 

  21. Hyvarinen A, Oja E (1997) A fast fixed-point algorithm for independent component analysis. Neural Comput 9:1483–1492

    Article  Google Scholar 

  22. Kano M, Hasebe S, Hashimoto I (2003) Evolution of multivariate statistical process control: application of independent component analysis and external analysis. In: Proceedings of the foundations of computer aided process operations conference (FOCAPO 2003), Coral Spring, USA, pp 385–388

  23. Kano M, Tanaka S, Hasebe S, Hashimoto I, Ohno H (2003) Monitoring independent components for fault detection. AIChE J 49:969–976

    Article  Google Scholar 

  24. Lee TW (1998) Independent component analysis: theory and application. Kluwer, Boston

    MATH  Google Scholar 

  25. Lee JM, Yoo C, Lee IB (2004) Statistical process monitoring with independent component analysis. J Process Control 14(5):467–485

    Article  Google Scholar 

  26. Lee JM, Yoo C, Lee IB (2003) On-line batch process monitoring using different unfolding method and independent component analysis. J Chem Eng Jpn 36:1384–1396

    Article  Google Scholar 

  27. Lee JM, Yoo CK, Lee IB (2003) New monitoring technique with an ICA algorithm in the wastewater treatment process. Water Sci Technol 47(12):49–56

    Google Scholar 

  28. Lee JM, Yoo CK, Lee IB (2004) Statistical monitoring of dynamic processes based on dynamic independent component analysis. Chem Eng Sci 59:2995–3006

    Article  Google Scholar 

  29. Lee JM, Qin SJ, Lee IB (2006) Fault detection and diagnosis based on modified independent component analysis. AIChE J 52:3501–3514

    Article  Google Scholar 

  30. Lu CW, Reynolds MR Jr (1999) EWMA control charts for monitoring the mean of autocorrelated processes. J Qual Technol 31:166–188

    Google Scholar 

  31. Luceno A (1995) Choosing the EWMA parameter in engineering process control. J Qual Technol 27:162–168

    Google Scholar 

  32. Macgregor JF (1990) A different view of the funnel experiment. J Qual Technol 22:255–259

    Google Scholar 

  33. Macgregor JF (1988) On-line statistical process control. Chem Eng Prog 84:21–31

    Google Scholar 

  34. Macgregor JF, Harris TJ, Wright JD (1984) Duality between the control of processes subject to randomly occurring deterministic disturbances and ARIMA stochastic disturbances. Technometrics 26:389–397

    Article  MathSciNet  Google Scholar 

  35. Montgomery DC, Mastrangelo CM (1991) Some statistical process control for autocorrelation data. J Qual Technol 23(3):179–193

    Google Scholar 

  36. Montgomery DC, Keats JB, Runger GC, Messina WS (1994) Integrating statistical process control and engineering process control. J Qual Technol 26(2):79–87

    Google Scholar 

  37. Montgomery DC (2001) Introduction to statistical quality control, 4th edn. Wiley, New York

    Google Scholar 

  38. Shao YE (1998) Integrated application of the cumulative score control chart and engineering process control. Stat Sinica 8:239–252

    MATH  Google Scholar 

  39. Shao YE, Chiu CC (1999) Developing identification techniques with the integrated use of SPC/EPC and neural networks. Qual Reliab Eng Int 15:287–294

    Article  Google Scholar 

  40. Shao YE, Runger GC, Haddock J, Wallace WA (1999) Adaptive controllers to integrate SPC and EPC. Commun Stat Simul Comput 28:13–36

    Article  Google Scholar 

  41. Steinberg D, Colla P (1997) CART—classification and regression trees. Salford Systems, San Diego

    Google Scholar 

  42. Stone JV (2004) Independent component analysis: a tutorial introduction. MIT, Cambridge

    Google Scholar 

  43. Stone JV, Porrill J, Porter NR, Wilkinson ID (2002) Spatiotemporal independent component analysis of event-related fMRI data using skewed probability density functions. NeuroImage 15(2):407–421

    Article  Google Scholar 

  44. He T, Clifford G, Tarassenko L (2006) Application of independent component analysis in removing artifacts from the electrocardiogram. Neural Comput Appl 15(2):105–116

    Article  Google Scholar 

  45. Wieringa JE (1999) Statistical process control for serially correlated data. PhD dissertation. University of Groningen, Netherlands

  46. Xia C, Howell J (2003) Isolating multiple sources of plant-wide oscillations via independent component analysis. Control Eng Pract 13(8):1027–1035

    Article  Google Scholar 

  47. Xia C (2003) Control loop measurement based isolation of faults and disturbances in process plants. PhD thesis. University of Glasgow, UK

  48. Yao ZX, Qian Y, Li XX, Jiang YB (2003) A description of chemical processes based on state space. In: Proceedings of the 8th international symposium on process systems engineering. Kunming, China, pp 1112–1117

  49. Zhao CH, Wang FL, Mao ZY, Lu NY, Jia MX (2008) Adaptive monitoring based on independent component analysis for multiphase batch processes with limited modeling data. Ind Eng Chem Res 47:3104–3113

    Article  Google Scholar 

Download references

Acknowledgments

This research was partially supported by the National Science Council of the Republic of China under Grant Number NSC 95-2221-E-027-072-MY3.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Chih-Chou Chiu.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Chiu, CC., Hwang, SY., Cook, D.F. et al. Process disturbance identification through integration of spatiotemporal ICA and CART approach. Neural Comput & Applic 19, 677–689 (2010). https://doi.org/10.1007/s00521-009-0324-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-009-0324-5

Keywords

Navigation