Root Cause Detection with an Ensemble Machine Learning Approach in the Multivariate Manufacturing Process

  • Deniz Demircioglu DirenEmail author
  • Semra Boran
  • Ihsan Hakan Selvi
  • Tugcen Hatipoglu
Conference paper
Part of the Lecture Notes in Management and Industrial Engineering book series (LNMIE)


Quality control in multivariate manufacturing processes should be applied with multi variate control charts. Although this method is sufficient, it doesn’t include the causes of uncontrolled situations. It only shows samples that are out of control. A variety of methods are required to determine the root cause(s) of the uncontrolled situations. In this study, a classification model, based on the ensemble approach of machine learning classification algorithms, is proposed for determining the root cause(s). Algorithms are compared according to predictive accuracy, kappa value and root square mean error rates as performance criteria. Results show that Neural Network ensemble techniques are more efficient and successful than individual Neural Network learning algorithms.


Multivariate process Multivariate quality control charts Mason young tracy decomposition Machine learning 


  1. Aparisi, F., & Sanz, J. (2010). Interpreting the out-of-control signals of multivariate control charts employing neural networks. International Journal of Computer, Electrical, Automation, Control and Information Engineering, 61, 226–230.Google Scholar
  2. Breiman, L. (1996a). Bias, variance, and arcing classifiers, technical report. Statistics Department, University Of California, Berkeley.Google Scholar
  3. Breiman, L. (1996b). Bagging predictors. Machine Learning, 24(2).zbMATHGoogle Scholar
  4. Çetin, S, & Birgören, B. (2007). Çok Değişkenli Kalite Kontrol Çizelgelerinin Döküm Sanayiinde Uygulanmasi. Journal of Faculty of Engineering and Architecture, 22(4). Gazi UniversityGoogle Scholar
  5. Chen, L. H., & Wang, T. Y. (2004). Artificial neural networks to classify mean shifts from multivariate χ2 chart signals. Computers & Industrial Engineering, 47, 195–205.CrossRefGoogle Scholar
  6. Cheng, C. S., & Cheng, H. P. (2008). Identifying the source of variance shifts in the multivariate process using neural networks and support vector machines. Expert Systems with Applications, 35, 198–206.CrossRefGoogle Scholar
  7. Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement (EPM), 20(1), 37–46.CrossRefGoogle Scholar
  8. Du, S., Lv, J., & Xi, L. (2012). On-line classifying process mean shifts in multivariate control charts based on multiclass support vector machines. International Journal of Production Research, 50(22), 6288–6310.CrossRefGoogle Scholar
  9. Freund, Y. (1999). A short introduction to boosting. Journal Japanese Society For Artificial Intelligence, 14(1), 771–780.MathSciNetGoogle Scholar
  10. 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, 475–493.CrossRefGoogle Scholar
  11. Han. J., Kamber. M., & Pei, J. (2012). Data mining concepts and techniques, 3rd ed., pp. 24–25.Google Scholar
  12. He, Q. P., & Wang, J. (2007). Fault detection using the k-nearest neighbor rule for semiconductor manufacturing processes. IEEE Transactions on Semiconductor Manufacturing, 20(4), 345–354.CrossRefGoogle Scholar
  13. He, S., Wang, G. A. W., & Zhang, M. (2013). Multivariate process monitoring and fault identification using multiple decision tree classifiers. International Journal of Production Research, 51(11), 3355–3371.CrossRefGoogle Scholar
  14. Hotelling, H. (1947). Multivariate quality control-illustrated by the air testing of sample bombsights. Techniques of Statistical Analysis, 2(5), 110–112.Google Scholar
  15. Karimi, P., & Rad-Jazayeri, H. (2014). Comparing the fault diagnosis performances of single neural network and two ensemble neural networks based on the boosting methods. Journal of Automation and Control, 2(1), 21–32.Google Scholar
  16. Kröse, B., & Smagt, P. (1996). An introduction to neural networks, 8th ed., pp. 23–37.Google Scholar
  17. Lehmann, E. L., & Casella, G. (2003). Theory of point estimation (2nd ed.). New York: Springer.zbMATHGoogle Scholar
  18. Mason, R. L., Tracy, N. D., & Young, C. H. (1995). Decomposition of T2 for multivariate control chart interpretation. Journal of Quality Technology, 27(2), 99–108.CrossRefGoogle Scholar
  19. Mason, R. L., Tracy, N. D., & Young, C. H. (1997). A practical approach for interpreting multivariate T2 control chart signals. Journal of Quality Technology, 29(4), 396–406.CrossRefGoogle Scholar
  20. Masood, I., & Hassan, A. (2013). Pattern recognition for bivariate process mean shifts using feature-based artificial neural network. International Journal of Advanced Manufacturing Technology, 66, 1201–1218.CrossRefGoogle Scholar
  21. Niaki, S. T. A., & Abbasi, B. (2005). Fault diagnosis in multivariate control charts using artificial neural networks. Quality and Reliability Engineering International, 21, 825–840.CrossRefGoogle Scholar
  22. Parra, M. G., & Loaiza, P. R. (2003). Application of the multivariate T2 control chart and the mason–tracy–young decomposition procedure to the study of the consistency of impurity profiles of drug substances. Journal Quality Engineering, 16(1).Google Scholar
  23. Salehi, M., Bahreininejad, A., & Nakhai, I. (2011). On-line analysis of out-of-control signals in multivariate manufacturing processes using a hybrid learning-based model. Neurocomputing, 7, 2083–2095.CrossRefGoogle Scholar
  24. Verron, S., Tiplica, T., & Kobi, A. (2006). Bayesian networks and mutual information for fault diagnosis of industrial systems, In Workshop on Advanced Control and Diagnosis (ACD’06).Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Deniz Demircioglu Diren
    • 1
    Email author
  • Semra Boran
    • 1
  • Ihsan Hakan Selvi
    • 1
    • 2
  • Tugcen Hatipoglu
    • 3
  1. 1.Industrial Engineering DepartmentSakarya UniversitySakaryaTurkey
  2. 2.Information Systems Engineering DepartmentSakarya UniversitySakaryaTurkey
  3. 3.Industrial Engineering DepartmentKocaeli UniversityKocaeliTurkey

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