Çok Değişkenli Proses Kontrol Grafiği ve Topluluk Makine Öğrenme Modeli Kullanılarak Kalite Kusurlarının Sınıflandırılması

Year 2024, Volume: 7 Issue: 2, 129 - 144, 26.09.2024

Deniz Demircioğlu Diren , Semra Boran

https://doi.org/10.38016/jista.1516453

Abstract

Çok değişkenli kontrol diyagramları birden fazla değişkenin etki ettiği süreçlerin izlenmesine olanak sağlamaktadır. Ancak süreç kontrol dışında olduğunda hangi değişkenin buna neden olduğunu tespit edilememektedir. Süreçteki hangi değişkenlerin düzeltici faaliyetlere ihtiyaç duyduğunu bilmek önemli bir gerekliliktir. Bu çalışmada süreci kontrolden çıkaran değişken/değişkenleri yüksek doğrulukla belirlenmesi tahmin etmek için makine öğrenmesi tabanlı bir model önerilmiştir. Bu amaçla tekli algoritmalara göre daha yüksek tahmin performansına sahip olduğu bilinen topluluk algoritmaları tercih edilmiştir. It is thought that a classification model in which ensemble algorithms are used together can increase the prediction accuracy. Daha önce bir kalite kontrol probleminde rastlanmayan model, gerçek bir probleme uygulanmış ve %98,06 sınıflandırma doğruluğu elde edilmiştir. Ayrıca bir diğer faydası da çok değişkenli kontrol grafiklerine ihtiyaç duymadan süreci kontrolden çıkaran değişken/değişkenleri tahmin edebilmesidir.

Keywords

Çok değişkenli kontrol grafiği, Makine öğrenmesi, Topluluk algoritması topluluğu, Hotelling T2 grafiği, Mason-Young-Tracy yöntemi.

Classification of Quality Defects using Multivariate Control Chart with Ensemble Machine Learning Model

Abstract

Multivariate control charts enable to monitor processes affected by more than one variable. But, when the process is out of control, it cannot detect which variable is causing it. It is an important requirement to know which variables in the process need corrective actions. In this study, a machine learning-based model is proposed to predict the variable/s that make the process out of control. For this purpose, ensemble algorithms, which are known to have higher prediction performance than single algorithms, were preferred. Because it is aimed to determine the variable(s) that cause the process to be out of control in the most accurate way. It is thought that a classification model in which ensemble algorithms are used together can increase the prediction accuracy. The model, which has not been encountered before in a quality control problem, was applied to a real problem and 98.06% classification accuracy was achieved. Another benefit is that it can predict the variable/variables that make the process uncontrolled without the need for multivariate control charts.

Keywords

Multivariate control chart, Machine learning, Ensemble of ensemble algorithm, Hotelling T2 chart, Mason-Young-Tracy method

References

• Agog, N. S., Dikko, H. G., Asiribo, O. E., 2014. Determining out-of-control variable(s) in a multivariate quality control chart. Sci. Africana, 13(2), 266–280.
• Agrawal, R., Imielinski, T., 1993. Swami, A., mining association rules between sets of items in large databases. ACM SIGMOD, 1–10.
• Ahsan, M., Mashuri, M., Lee, M. H., Kuswanto, H., Prastyo, D. D. 2020. Robust adaptive multivariate Hotelling's T2 control chart based on kernel density estimation for intrusion detection system. Expert Systems with Applications, 145, 113105.
• Alfaro, E., Alfaro, J.L., Gamez M., Garcia N., 2009. A boosting approach for understanding out-of-control signals in multivariate control charts. Int. J. Prod. Res., 47(24), 6821–6834.
• Alpaydın, E., 2012. Yapay Öğrenme. 3. Edition. Boğaziçi University, 207-341.
• Anwar, H., Qamar, U. Qureshi, A. W. M., 2014. Global optimization ensemble model for classification methods. Sci. World J., 1-9.
• Aparisi, F., Avendaño, G., Sanz, J., 2006. Techniques to interpret T2 control chart signals. IIE Trans., Institute Ind. Eng., 38(8), 647–657.
• Asadi, A., Farjami Y., 2019. Online mean shift detection in multivariate quality control using boosted decision tree learning. J. Syst. Manag., vol. 2, 081–106.
• Bersimis, S., Sgora, A., Psarakis, S. 2022. A robust meta‐method for interpreting the out‐of‐control signal of multivariate control charts using artificial neural networks. Quality and Reliability Engineering International, 38(1), 30-63.
• Bilgin, M., 2018. Veri Biliminde Makine Öğrenmesi Makine Öğrenmesi Teorisi ve Algoritmaları. 2. Edition Papatya Bilim, 31-138.
• Blagus, R., Lusa, L., 2013. SMOTE for high-dimensional class-imbalanced data. BMC Bioinformatics, 14(16), 1471–2103.
• Boullosa, D., Larrabe, J. L., Lopez, A., Gomez M. A., 2017. Monitoring through T2 Hotelling of cylinder lubrication process of marine diesel engine. Appl. Therm. Eng., 110, 32–38.
• Breiman, L. 1996. Bagging predictors. Machine learning, 24, 123-140.
• Çetin, S., Birgören B., 2007. Çok deǧi̇şkenli̇ kali̇te kontrol çi̇zelgeleri̇ni̇n döküm sanayi̇inde uygulanmasi. Gazi Üniv. Müh. Mim. Fak. Der., 22(4), 809–818.
• Chawla, N. V., Bowyer, K. W., Hall, L. O., Kegelmeyer, W. P., 2002. SMOTE: Synthetic minority over-sampling technique. J. Artif. Intell. Res., 16, 321-357.
• Chen. L. H., Wang T. Y., 2004. Artificial neural networks to classify mean shifts from multivariate χ2 chart signals. Comput. Ind. Eng., 47(2–3), 195–205.
• 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 Syst. Appl., 35(1–2),198–206.
• Cheng, C.S., Lee H.T., 2012. Identifying the out-of-control variables of multivariate control chart using ensemble SVM classifiers. J. Chinese Inst. Ind. Eng., 29(5), 314–323.
• Cortes, C., Vapnik, V., 1995. Support-vector networks. Mach. Learn., 20(3), 273–297.
• Das, N., Prakash, V., 2008. Interpreting the out-of-control signal in multivariate control chart — a comparative study. Int. J. Adv. Manuf. Technol., 37, 966–979.
• Dreiseitl, S., Machado, O, L., Kittler, H., Vinterbo, S., Billhardt, H., Binder, M., 2001. A comparison of machine learning methods for the diagnosis of pigmented skin lesions. J. Biomed. Inform., 34(1), 28-36.
• Du, S., Lv, J., Xi, L., 2012. On-line classifying process mean shifts in multivariate control charts based on multiclass support vector machines. Int. J. Prod. Res., 50(22), 6288–6310.
• Farhan, S., Fahiem, M. A., Tauseef, H., 2014. An ensemble-of-classifiers based approach for early diagnosis of alzheimer’s disease: Classification using structural features of brain images. Comput. Math., Methods Med., 2014.
• Gowda, S., Kumar, H., Imran, M., 2018. Ensemble based learning with stacking. Boosting and Bagging for Unimodal Biometric Identification System, 30-36.
• 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. Comput. Ind. Eng., 55(2), 475–493.
• Han, J., Kamber, M., Pei, J., 2012. Data mining. concepts and techniques. The Morgan Kaufmann Series in Data Management Systems, 3. Edition.
• Hawkins, D. M., 1991. Multivariate quality control based on regression-adiusted variables. Technometrics, 33(1), 61–75.
• Hossin, M, Sulaiman, M., N, 2015. A review on evaluation metrics for data classification evaluations. Int. J. Data Min. Knowl. Manag. Process, 5(2), 01–11.
• Hotelling H., Multivariable quality control—illustrated by the air testing of sample bombsight, McGraw Hill, 111-184, 1947.
• Hu, L. Y., Huang, M. W., Ke, S. W., Tsai, C. F., 2016. The distance function effect on k-nearest neighbor classification for medical datasets. Springerplus, 5(1).
• Huda, S., Abdollahian, M., Mammadov, M., Yearwood, J., Ahmed S., Sultan I., 2014. A hybrid wrapper-filter approach to detect the source(s) of out-of-control signals in multivariate manufacturing process. Eur. J. Oper. Res., 237(3), 857–870.
• Jackson, J. E., 1985. Multivariate quality control. Commun. Stat. Theory Methods, 14(11), 2657–2688.
• Jiang, J., Song, H.-M., 2017. Diagnosis of out-of-control signals in multivariate statistical process control based on bagging and decision tree. Asian Bus. Res., 2(2).
• Jonathan, O., Omoregbe, N., Misra, S., 2019. Empirical comparison of cross-validation and test data on internet traffic classification methods. Journal of Physics: Conference Series, 1299(1), 1-9.
• Joshi, K., Patil, B. 2022. Multivariate statistical process monitoring and control of machining process using principal component-based Hotelling T2 charts: A machine vision approach. International Journal of Productivity and Quality Management, 35(1), 40-56.
• Karimi, S., Yin, J., Baum, J., 2015. Evaluation methods for statistically dependent text. Comput. Linguist., 41(3), 539–548.
• Lantz, B., 2013. Machine learning with R: learn how to use R to apply powerful machine learning methods and gain an insight into real-world applications. Birmingham: Packt Publishing Ltd; 66-343.
• Li, J., Jin, J., Shi, J., 2008. Causation-based T2 decomposition for multivariate process monitoring and diagnosis. J. Qual. Technol., 40 (1), 46–58.
• Li, T., Hu, S., Wei, Z., Liao, Z., 2013. A framework for diagnosing the out-of-control signals in multivariate process using optimized support vector machines. Math. Probl. Eng., 2013(2), 1–9.
• Lowry, C. A., Woodall, W. H., Champ, C. W., Rigdon, S. E., A multivariate exponentially weighted moving average control chart, Technometrics, 34(1), 46–53, 1992.
• Lu, C. J., Shao, Y. E., Li, P. H., 2011. Mixture control chart patterns recognition using independent component analysis and support vector machine. Neurocomputing, 74(11), 1908-1914.
• Maimon, L., Rokach, O., 2010. Data mining and knowledge discovery handbook. 2. Edition. Springer London, 165-174.
• Maleki, M. R., Amiri, A., 2015. Simultaneous monitoring of multivariate-attribute process mean and variability using artificial neural networks. J. Qual. Eng. Prod. Optim., 1(1), 43–54.
• Mason, R. L., Champ, C. W., Tracy, N. D., Wierda, S. J., & Young, J. C. (1997). Assessment of multivariate process control techniques. Journal of quality technology, 29(2), 140-143.
• Mason, R. L., Tracy, N. D., Young, J. C., 1995. Decomposition of T2 for multivariate control chart interpretation. J. Qual. Technol., 27(2), 99–108.
• Mitchell, T. M., 2014. Machine learning. McGraw-Hill Science, 52-155.
• Mohammed, M., Khan, M. B., Bashier, E. B. M., 2016. Machine learning: Algorithms and applications. 1. Edition. CRC Press, 5-11.
• Montgomery D. C., 2009. Introduction to statistical quality control. 6. Edition. John Wiley & Sons, 499-507.
• Niaki, S. T. A., Abbasi. B., 2005. Fault diagnosis in multivariate control charts using artificial neural networks. Qual. Reliab. Eng. Int., 21(8), 825–840.
• Onan, A., 2018. Particle swarm optimization based stacking method with an application to text classification. Acad. Platf. J. Eng. Sci., 6(2), 134–141.
• Onel, M., Kieslich, C. A., Pistikopoulos, E. N., 2019. A nonlinear support vector machine-based feature selection approach for fault detection and diagnosis: Application to the Tennessee Eastman process. AIChE J., 65(3), 992–1005.
• Özel, S. 2005. Çok değişkenli kalite kontrolün döküm sanayiinde uygulanması, Master’s Thesis, Kırıkkale University, YOK Thesis Center.
• Öztemel E., 2003. Yapay Sinir Ağları. İstanbul, Papatya Yayınları, 7.
• Parra, M. G., P. Loaiza, R., 2003. Application of the multivariate T2 control chart and the Mason Tracy Young decomposition procedure to the study of the consistency of ımpurity profiles of drug substances. Qual. Eng., 16(1), 127–142.
• Pei, X., Yamashita, Y., Yoshida, Matsumoto, M., S., 2006. Discriminant analysis and control chart for the fault detection and identification. Comput. Aided Chem. Eng.,21, 1281-1286.
• Rakhmawan, S. A., Omar, M. H., Riaz, M., Abbas, N. 2023. Hotelling T2 control chart for detecting changes in mortality models based on machine-learning decision tree. Mathematics, 11(3), 566.
• Ramezan, C. A., Warner, T. A., Maxwell, A. E., 2019. Evaluation of sampling and cross-validation tuning strategies for regional-scale machine learning classification. Remote Sens., 11(185), 1-22.
• Rao, O. R. M., Subbaiah, K.V., Rao, K. N., Rao T. S., 2013. Application of multivariate control chart for improvement in quality of hotmeal-a case study. Int. J. Qual. Res., 7(4), 623–640.
• Refaeilzadeh, P., Tang, L., Liu, H., 2009. C Cross-validation. Springer, Boston, 1-3.
• Robert J. C. Y., Mason L., 2002. Multivariate statistical process control with industrial applications. Society for Industrial and Applied Mathematics, 5-17.
• Rokach, L., 2010. Ensemble-based classifiers. Artif. Intell. Rev., 33(1–2), 1–39.
• Sabahno, H., Amiri, A. 2023. New statistical and machine learning based control charts with variable parameters for monitoring generalized linear model profiles. Computers & Industrial Engineering, 184, 109562.
• Salehi, M., Kazemzadeh, R. B., Salmasnia, A., 2012. On line detection of mean and variance shift using neural networks and support vector machine in multivariate processes. Appl. Soft Comput. J., 12(9), 2973–2984.
• Shao, Y. E., Lin, S. C., 2019. Using a time delay neural network approach to diagnose the out-of-control signals for a multivariate normal process with variance shifts. Mathematics, 7(10).
• Şişci, M., Torkul, Y. E., Selvi, İ. H. 2022. Machine learning as a tool for achieving digital transformation. Knowledge Management and Digital Transformation Power, 55.
• Song, H., Xu, Q., Yang, H., Fang, J., 2017. Interpreting out-of-control signals using instance-based bayesian classifier in multivariate statistical process control. Commun. Stat. Simul. Comput., 46(1).
• The Royal Society, 2017. Machine learning: the power and promise of computers that learn by example, 5-6.
• Ulen, M., Demir, I., 2013. Application of multivariate statistical quality control in pharmaceutical industry. Balk. J. Math.,1, 93–105.
• Utgoff, P. E. Berkman, N. C., Clouse, J. A., 1997. Decision Tree Induction Based on Efficient Tree Restructuring. Kluwer Academic Publishers, 29, 5-44.
• Woodall W. H., Ncube M. M., Multivariate CUSUM quality-control procedures, technometrics, 27(3), 285–292, 1985.
• Yadav, M., Yadav, A., Kumar N., 2015. An introduction to neural network methods for differential equations. Springer.
• Yang, W. A., 2015. Monitoring and diagnosing of mean shifts in multivariate manufacturing processes using two-level selective ensemble of learning vector quantization neural networks. J. Intell. Manuf., 26(4), 769–783.
• Yılmaz, H., 2012. Çok değişkenli istatistiksel süreç kontrolü: Bir hastane uygulaması, Master’s Thesis, İstanbul Teknik University, YOK Thesis Center.
• Yu, J. Bo., Xi, L. Feng., 2009. A neural network ensemble-based model for on-line monitoring and diagnosis of out-of-control signals in multivariate manufacturing processes. Expert Syst. Appl., 36(1), 909–921.
• Yu, Y., Feng, Y., 2014. Modified cross-validation for penalized high-dimensional linear regression models. J. Comput. Graph. Stat., 23(4), 1009-1027.
• Zhang, Y., Li, M., Han, S., Ren, Q., Shi, J., 2019. Intelligent identification for rock-mineral microscopic images using ensemble machine learning algorithms. Sensors, 19(9), 1-14.
• Zhang, Y., Ma, C., 2012. Ensemble machine learning. Springer US.
• Zhou, Z. H., 2012. Ensemble methods: foundations and algorithms Chapman & Hall/CRC Data Mining and Knowledge Discovery Series, Taylor & Francis.