Integration of machine learning techniques and control charts in multivariate processes

Document Type : Article

Authors

Department of Industrial Engineering, Sakarya University, Sakarya, 54050, Turkey

Abstract

Using multivariate control chart instead of establishing univariate control chart for all variables in processes provides time and labor advantage. In addition, it is considered in the relations between variables. However, the statistical calculation of the measured values of all variables is seen as a single value in the control chart. Therefore, it is necessary to determine which variable(s) is the cause of the out of control signal. Effective corrective measures can only be developed when the causes of the fault(s) are determined correctly. The aim of the study is to determine the machine learning techniques that will accurately estimate the type of fault. With the Hotelling T2 chart, out of control signals are identified and the types of faults affected by the variables are defined. Various machine learning techniques are used to compare classification performances. The developed model was applied in the evaluation of the paint quality in a painting process. ANN was determined as the most successful techniques according to performance criteria. The novelty of the study is to classify the fault according to the types of faults, not the variables. Defining the faults according to its types will enable to take effective corrective actions quickly.

Keywords

Main Subjects


  1. Refrences:

    1. Hotelling, H. Multivariate quality control illustrated by air testing of sample bombsights", Techniques of Statistical Analysis, 2(5), pp. 111{152 (1947).
    2. Woodall, W.H. and Ncube, M.M. Multivariate cusum quality control procedures", Technometrics, 27(3), pp. 285{292 (1985).
    3. Lowry, A.A., Woodall, W.H., Champ, C.W., et al. A multivariate exponentially weighted moving average control chart", Technometrics, 34(1), pp. 46{53 (1992).
    4. Murphy, B.J. Selecting out of control variables with the T2 multivariate quality control procedure", The Statistician, 36, pp. 571{583 (1987).
    5. Mason, R.L., Tracy, N.D., and Young, C.H. Decomposition of T2 for multivariate control chart interpretation", Journal of Quality Technology, 27(2), pp. 99{ 108 (1995). 6. Kourti, T. and MacGregor J.F. Multivariate SPC methods for process and product monitoring", Journal of Quality Technology, 28(4), pp. 409{428 (1996). 7. Li, J., Jin, J., and Shi, J. Causation-based T2 decomposition for multivariate process monitoring and diagnosis", Journal of Quality Technology, 40(1), pp. 46{58 (2008). 8. Midany, T.E., Baz, A.A.E., and Elwahed, M.S.A. A proposed framework for control chart pattern recognition in multivariate process using arti_cial neural networks", Expert Systems with Applications, 37(2), pp. 1035{1042 (2010). 9. Addeh, A., Khormali, A., and Golilarz, N.A. Control chart pattern recognition using RBF neural network with new training algorithm and practical features", ISA Transactions, 79, pp. 202{216 (2018). 10. Wang, X. Hybrid abnormal patterns recognition of control chart using support vector machining", International Conference on Computational Intelligence and Security, pp. 238{241 (2008). 11. Li, T.L., Hu, S., Wei, Z., and Liao, Z. A framework for diagnosis the out of control signals in multivariate process using optimized support vector machine", Mathematical Problems in Engineering, 2013(2), pp. 1{9 (2013). 12. Xanthopoulos, P. and Razzaghi, T. A weighted support vector machine method for control chart pattern recognition", Computers & Industrial Engineering, 70, pp. 134{149 (2014). 13. Wang, C.H., Dong, T.P., and Kuo, W. A hybrid approach for identi_cation of concurrent control chart patterns", Journal of Intelligent Manufacturing, 20(4), pp. 409{419 (2009). 14. Lu, C.J., Shao, Y.E., and Li, P.H. Mixture control chart patterns recognition using independent component analysis and support vector machine", Neurocomputing, 74(11), pp. 1908{1914 (2011). 15. Zhang, M. and Cheng, W. Recognition of mixture control chart pattern using multiclass support vector machine and genetic algorithm based on statistical and shape features", Mathematical Problems in Engineering, 2015(5), pp. 1{10 (2015). 3240 D. Demircio_glu Diren et al./Scientia Iranica, Transactions E: Industrial Engineering 27 (2020) 3233{3241 16. Chen, L.H. andWang, T.Y. Arti_cial neural networks to classify mean shifts from multivariate _2 chart signals", Computers and Industrial Engineering, 47(2{ 3), pp. 195{205 (2004). 17. Niaki, S.T.A. and Abbasi, B. Fault diagnosis in multivariate control charts using arti_cial neural networks", Quality and Reliability Engineering International, 21, pp. 825{840 (2005). 18. Aparisi, F. and Sanz, J. Interpreting the out-ofcontrol signals of multivariate control charts employing neural networks", International Journal of Computer and Information Engineering, 4(1), pp. 24{28 (2010). 19. Atashger, K. and Noorossana, R. An integrating approach to root cause analysis of a bivariate mean vector with a linear trend disturbance", The International Journal of Advanced Manufacturing Technology, 52(1{ 4), pp. 407{420 (2011). 20. Masood, I. and Hassan, A. Pattern recognition for bivariate process mean shifts using feature-based arti- _cial neural network", The International Journal of Advanced Manufacturing Technology, 66(9{12), pp. 1201{1218 (2013). 21. Du, S., Lv, J., and Xi, L. On-line classifying process mean shifts in multivariate control charts based on multiclass support vector machines", International Journal of Production Research, 50(22), pp. 6288{6310 (2012). 22. Guh, R-S. and Shiue, Y-R. An e_ective application of decision tree learning for on-line detection of mean shifts in multivariate control charts", Computers and Industrial Engineering, 55(2), pp. 475{493 (2008). 23. He, S., Wang, G.A., Zhang, M., et al. Multivariate process monitoring and fault identi_cation using multiple decision tree classi_ers", International Journal of Production Research, 51(11), pp. 3355{3371 (2013). 24. Jiang, J. and Song, H.M. Diagnosis of out-of-control signals in multivariate statistical process control based on bagging and decision tree", Asian Business & Management, 2(2), pp. 1{6 (2017). 25. Jadhav, S.D. and Channe, H.P. Comparative study of K-NN, Naive Bayes and decision tree classi_cation techniques", International Journal of Science and Research, 5(1), pp. 1842{1845 (2016). 26. Cheng, C.S. and Cheng, H.P. Identifying the source of variance shifts in the multivariate process using neural networks and support vector machines", Expert Systems with Applications, 35(1{2), pp. 198{206 (2008). 27. Salehi, M., Bahreininejad, A., and Nakhai, I. Online analysis of out-of-control signals in multivariate manufacturing processes using a hybrid learning-based model", Neurocomputing, 74(12{13), pp. 2083{2095 (2011). 28. Ceylan, Y., Usta, K., Yumurtac_, H., et al. An ESR study on 22, 4 diaminotoluene exposed to gamma rays and application of machine learning", Acta Physica Polonica A, 130(1), pp. 184{187 (2016). 29. Moore, A. and Zuev, D. Internet tra_c classi_cation using Bayesian analysis techniques", Sigmetrics, 33(1), pp. 50{60 (2005). 30. Murakami, Y. and Mizuguchi, K. Applying the naive Bayes classi_er with kernel density estimation to the prediction of protein-protein interaction sites", Bioinformatics, 26(15), pp. 1841{1848 (2010). 31. Kuter, S., Usul, N., and Kuter, N. Bandwidth determination for kernel density analysis of wild_re events at forest sub-district scale", Ecological Modelling, 222(17), pp. 3033{3040 (2011). 32. Fix, E. and Hodges, J. Discriminatory analysis. nonparametric discrimination: consistency properties", Technical Report 4, USAF School of Aviation Medicine, Randolph Field, Texas, USA (1951). 33. Williams, J.W. and Li, Y. Comparative study of distance functions for nearest neigbor", Advanced Techniques in Computing Sciences and Software Engineering, Elleithy, Khaled, Ed., pp. 79{84 (2010). 34. Kataria, A. and Singh, M. A review of data classi_cation using K-nearest neigbor algorithm", International Journal of Emerging Technology and Advanced Engineering, 3(6), pp. 354{360 (2013). 35. Yuksel, A.S, C_ ankaya, S.F. and Uncu, _I.S. Design of a machine learning based predictive analytics system for spam problem", Acta Physica Polonica A, 132(1), pp. 500{504 (2017). 36. Quinlan, J.R. Improved use of continuous attributes in C4.5", Journal of Arti_cial Intelligence Research, 4, pp. 77{90 (1996). 37. Mitchell, T.M., Machine Learning, McGraw-Hill Science/ Engineering/Math, 81 (1997). 38. Tsai, K-M. and Luo, H-J. An inverse model for injection molding of optical lens using arti_cial neural network coupled with genetic algorithm", Journal of Intelligent Manufacturing, 28(2), pp. 473{487 (2017). 39. Alpayd_n, E., Introduction to Machine Learning, Second Edn., The MIT Press, England, pp. 203{247 (2010). 40. Hinton, G.E., Osindero, S., and Teh, Y-W. A fast learning algorithm for deep belief nets", Neural Computation, 18, pp. 1527{1554 (2006). 41. Vargas, R., Mosavi, A., and Ruiz, R. Deep learning: A review", Advances in Intelligent Systems and Computing, 5(2), pp. 53040{53065 (2017). 42. LeCun, Y., Bengio, Y., and Hinton, G. Deep learning", Nature, 521, pp. 436{444 (2015). 43. Seker, S.E. and Ocak, I. Performance prediction of roadheaders using ensemble machine learning techniques", Neural Computing and Applications, 31(4), pp. 1103{116 (2019). 44. Rodrigues, E.O., Pinheiro, V.H.A., Liatsis, P., et al. Machine learning in the prediction of cardiac epicardial and mediastinal fat volumes", Computers Biology and Medicine, 89, pp. 520{529 (2017). D. Demircio_glu Diren et al./Scientia Iranica, Transactions E: Industrial Engineering 27 (2020) 3233{3241 3241 45. Haji, M.M. and Katebi, S.D. Machine learning approaches to text segmentation", Scientia Iranica, A, 13(4), pp. 395{403 (2006). 46. Rabiei, A., Naja-Jilani, A., and Zakeri-Niri. M. Application of neural network models to improve prediction accuracy of wave run-up on antifer covered breakwater", Scientia Iranica, A, 24(2), pp. 567{575 (2017). 47. Peng, X. and Dai, J. A bibliometric analysis of neutrosophic set: two decades review from 1998 to 2017", Arti_cial Intelligence Review, 53, pp. 199{255 (2020). 48. Peng, X. and Selvachandran, G. Pythagorean fuzzy set: State of the art and future directions", Arti_cial Intelligence Review, 52, pp. 1873{1927 (2019).