Kenan ORÇANLI, Erkan OKTAY, Burak BİRGÖREN

3823

Çok Değişkenli Kontrol Kartları Örüntü Tanıma Literatüründe Bir Araştırma

İstatistiksel süreç kontrolünde kullanılan çok değişkenli kontrol grafikleri, çok değişkenli bir süreçteki anormal değişimleri incelemede önemli bir araçtır. Süreçte zaman içinde oluşabilecek değişimlerin tespit edilmesi, sürecin kontrol altında tutulması ve gerekli önlemlerin alınması amacıyla süreçteki anormal değişimlerin örüntülerini tanımlamaya yönelik son zamanlarda Yapay Sinir Ağları tanıma sistemleri oldukça fazla kullanılmaktadır. Yapay Sinir Ağları (YSA), giriş verilerinin yetersiz olduğu, mevcut verilerden hareketle bilinmeyen ilişkilerin ortaya çıkarılması ve algoritması veya kuralları tam olarak bilinmeyen durumlar için geliştirilmiş bir bilgi işleme sistemidir. Desen tanıma, tahmin, sınıflandırma gibi pek çok kalite kontrol problemi için yapay sinir ağları kullanılmaktadır. Yapay sinir ağı yaklaşımı ile birlikte kalite kontrol faaliyetleri daha kolay olmakta, maliyetler ve muayene süreleri minimize edilebilmektedir. Kontrol kartlarında yapay sinir ağlarıyla örüntü tanıma konusunda 1980 yılları sonları ile 1990 yılları sonları arasında yazılmış dokümanlar Zorriassatine ve Tannock (1998) tarafından ve 1991 ile 2010 yılları arasındaki zamanda yazılmış dokümanlar ise Psakaris (2011) ve Hachicha ve Ghorbelb (2012) tarafından incelenmiştir. Ayrıca, Masood ve Hassan (2010) tarafından bir inceleme metodolojisi oluşturulmuştur. Yapılan araştırmalar daha çok tek değişkenli ve çok değişkenli kontrol kartlarını kapsayacak şekilde genel nitelikli araştırmalardır. Bu çalışmada ise yapay sinir ağlarının 1990 ile 2015 yılları arasındaki diğer dokümanlardan farklı olarak çok değişkenli kontrol kartlarındaki uygulamalar derinliğine araştırılmıştır. Araştırmanın sonucunda ise genel olarak özetle, çok değişkenli kontrol kartlarında kullanılan YSA modellerinde teorik gelişmelerin olmadığı, tek YSA modeli yerine birleşik YSA modellerinin kullanıldığı ve YSA modelleri ile birlikte diğer sınıflandırma yöntemlerinin özellikle destek vektör makinelerinin hibrit olarak kullanıldığı şeklinde sonuçlara ulaşılmıştır. Yapılan araştırma çok değişkenli kontrol kartlarında yapay sinir ağlarını kullanmak isteyen araştırmacılar için bir başlangıç noktası olacağı değerlendirilmektedir. 
Anahtar Kelimeler:

Kontrol Kartları, Örüntü Tanıma, Yapay Sinir Ağları, Literatür

A Survey Of Multivariate Control Chart Pattern-Recognition Literature

Multivariate control charts for statistical process control are important tools to examination abnormal changes in a multivariate process. Recently, Artificial Neural Networks pattern recognition systems are examined to identify patterns of abnormal changes in a process to identify changes that may occur over time, to keep a process under control and to take necessary actions in a process. Artificial Neural Networks are input data systems which are developed for the cases in which are starting from the available data to disclose the unknown relationships and the algorithm or data processing system in which rules are not exactly known. Neural networks are used a number of multivariate quality control charts, such as pattern recognition, forecasting, classification. Quality control functions are became easier, and the cost of it and time for inspection is minimized by using neural network approach. Three comprehensive literature review of ANN applications for SPC are provided, reviewing works from the late 1980s to the late 1990s by Zorriassatine and Tannock (1998) and from 1991 to 2010 by Psakaris (2011) and Hachicha and Ghorbelb (2012). Besides, a research metadology is formed by Masood ve Hassan (2010). In this paper, artificial neural network applications for multivariate quality control charts are reviewed between 1990 and 2015. In summarizing the results of research, there is no development about Artificial Neural Network Model in multivariate control chart, Mixtured ANN models are used instead of single ANN models and ANN models are used as a hybrid of with other classification method, especially support vector machine. I think that this research will be a start point about ANN models in multivariate control charts for new beginner. sonuçlara ulaşılmıştır. Yapılan araştırma çok değişkenli kontrol kartlarında yapay sinir ağlarını kullanmak isteyen araştırmacılar için bir başlangıç noktası olacağı değerlendirilmektedir.
Keywords:

Control Charts, Pattern Recognition, Artificial Neural Networks, Literature,

PDF

___

  • Ahmadzadeh, F. (2010). Change Point Detection with Multivariate Control Charts by Artificial Neural Network. International Journal of Advanced Manufacturing Technology.
  • Al-Assaf, Y. (2004). Recognition of Control Chart Patterns Using Multi-Resolution Wavelets Analysis and Neural Networks. Comput Ind Eng 47:17–29.
  • Alt, F.B. (1985). Multivariate Quality Control. The Encyclopedia of Statistical Sciences, Wiley: New York, 110-122.
  • Al-Ghanim, A.M., (1997). An Unsupervised Learning Neural Algorithm for Identifying Process Behaviour on Control Charts and a Comparison with Supervised Learning
  • Approaches. Computers and Industrial Engineering 32, 627-639.
  • Aparisi, F, Jabaloyes, J. ve Carrion, A. (2001). Generalized Variance Chart Design with Adaptive Sample Sizes. The Bivariate Case. Communications in Statistics Theory and Methods. 30:931--948.
  • Aparisi, F, Avendano, G. ve Sanz, J. (2006). Techniques to Interpret T2 Control Chart Signals. IIE Transactions. 38:647-657.
  • Assaleh, K. ve Al-Assaf, Y. (2005). Features Extraction and Analysis for Classifying Causable Patterns in Control Charts. Comput Ind Eng 49:168–181.
  • Atashgar, K, ve Noorossana, R. (2011). An Integrating Approach to Root Cause Analysis of
  • A Bivariate Mean Vector with A Linear Trend Disturbance. International Journal of Advanced Manufacturing Technology. 52:407-420.
  • Büyüköztürk, Ş. ve diğerleri (2010). Bilimsel Araştırma Yöntemleri, 7.Baskı, PEGEM Akademi. Ankara.
  • Chen, Z., Lu, S., ve Lam, S., (2007). A Hybrid System for SPC Concurrent Pattern Recognition. Advanced Engineering Informatics 21, 303-310.
  • Chen, L.H. ve Wang, T.Y. (2004). Artificial Neural Networks to Classify Mean Shifts from Multivariate χ2 Chart Signals. Computers and Industrial Engineering 47, 195-205.
  • Cheng, C.S. ve 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.
  • Cheng, C. S. (1997). A Neural Network Approach for The Analysis of Control Chart Patterns. International Journal of Production Research, 35(3), 667–697
  • Cheng, C.S. ve Cheng, H.P. (2010). Using Neural Networks to Detect The Bivariate Process Variance Shifts Pattern. Computers and Industrial Engineering.
  • Chen, L.H. ve Wang, T.Y. (2004). Artificial Neural Networks to Classify Mean Shifts from Multivariate Chart Signals. Computers and Industrial Engineering. 47:195-205.
  • Chen, Z, Lu, S. ve Lam, S. (2007). A Hybrid System for SPC Concurrent Pattern Recognition. Adv Eng Inform 21:303–310
  • Cheng, C. S. (1997). A Neural Network Approach for The Analysis of Control Chart Patterns. International Journal of Production Research, 35(3), 667–697.Cheng, C.S. ve 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.
  • Cohen, L., Manion, L. ve Morrison, K. (2007). Research Methods in Education(6th Edition). London: Routledge Falmer.
  • Dalkey, N. C. ve Helmer, O. (1963). An Experimental Application of The Delphi Method to The Use of Experts. Management Science, 9(3), 458-467.
  • Du, S, Lv, J. ve Xi, L. (2010). An Integrated System for On-Line Intelligent Monitoring and Identifying Process Variability and Its Application. International Journal of Computer Integrated Manufacturing. 23:529-542.
  • Ducan, A. J. (1986). Quality control and industrial statistics(5th ed). Homewood, IL:Richard D. Irwin.
  • El-Midany, T.T., El-Baz, M.A. ve Abd-Elwahed, M.S. (2010). A Proposed Framework for Control Chart Pattern Recognition in Multivariate Process Using Artificial Neural Networks. Expert Systems with Applications, 37:1035-1042.
  • Gauri, S.K. ve Chakraborty, S. (2006). Feature-Based Recognition of Control Chart Patterns. Comput Ind Eng 51:726–742
  • Gauri, S.K. ve Chakraborty, S. (2008). Improved Recognition of Control Chart Patterns Using Artificial Neural Networks. Int J Adv Manuf Technol 36:1191–1201
  • Grigoryan, A. ve He, D. (2005). Multivariate Double Sampling|S| Charts for Controlling Process Variability. International Journal of Production Research.;43:715--730.
  • Guh, R.S., (2007). On-Line Identification and Quantification of Mean Shifts in Bivariate Processes Using a NN-Based Approach. Quality and Reliability Engineering International 23, 367-385.
  • Guh, R.S. ve Tannock, J.D.T. (1999). Recognition of Control Chart Concurrent Patterns Using a Neural Network Approach. International Journal of Production Research 37, 1743- 1765.
  • Guh, R.S. (2007). On-Line Identification and Quantification of Mean Shifts in Bivariate Processes Using a Neural Network-Based Approach. Quality and Reliability Engineering International;23:367-385.
  • Guh, R.S., Tannock, J.D.T. ve O’Brien, C. (1999). Intellispc: A Hybrid Intelligence Tool for On-Line Economical Statistical Process Control. Expert System with Applications17, 195- 212.
  • Guh, R.S., Zoriassatine, F., Tannock, J.D.T., ve O’Brien, C. (1999). On-Line Control Chart Pattern Detection and Discrimination-a Neural Network Approach. Artificial Intelligence in Engineering13, 413-425.
  • Guh, R.S. ve Hsieh, Y.C. (1999). A Neural Network Based Model for Abnormal Pattern Recognition of Control Charts. Computers and Industrial Engineering 36, 97-108
  • Guh, R.S. (2010). Simultaneous Process Mean and Variance Monitoring Using Artificial Neural Networks. Comput Ind Eng 58:739–753
  • Hachica, W. ve Ghorbel, A. (2012). A Survey of Control-Chart Pattern-Recognition Literature (1991-2010) Based on A New Conceptual Classification Scheme. Computers and Industrial Engineering 63: 204-222.
  • Hassan, A, Nabi Baksh, M.S., Shaharoun, M.A. ve Jamaludin, H. (2003). Improved SPC Chart Pattern Recognition Using Statistical Features. Int J Prod Res 41(7):1587–1603
  • Hwarng, H.B. ve Hubele, N. F. (1991). X-Bar Chart Pattern Recognition Using Neural Nets. ASQC Quality Congress Transactions, 884-889.
  • Hwarng, H.B. ve Hubele, N.F. (1993). Back-Propagation Pattern Recognizers for X-Bar Control Charts: Methodology and Performance. Computers and Industrial Engineering 24, 219-235.
  • Hwarng, H.B. (1995a). Proper and Effective Training of a Pattern Recognizerfor Cyclic Data. IEEE Transactions 27, 746-756.
  • Hwarng, H.B. (1995b). Multilayer Perceptron for Detecting Cyclic Data on Control Charts. International Journal of Production Research33, 3101-3117.
  • Hwarng, H.B. (1997). A Neural Network Approach to Identifying Cyclic Behaviour on Control Charts. International Journal of System Science 28, 99-112.
  • Hwarng, H.B. (2008). Toward İdentifying the Source of Mean Shifts in Multivariate SPC: A Neural Network Approach. International Journal of Production Research 46:5531-5559.
  • Hwarng, H.B. ve Wang, Y. (2010). Shift Detection and Source İdentification in Multivariate Autocorrelated Processes. International Journal of Production Research. 48:835--859. Işığıçok, E. (2004). Toplam Kalite Kontrol Bakış Açısı ile İstatistiksel Kalite Kontrol. Ezgi Kitapevi, Bursa.
  • Kaya, İ. ve Engin, O. (2005). Kalite İyileştirme Sürecinde Yapay Zekâ Tekniklerinin Kullanımı. Pamukkale Unıversıty Engıneerıng College Mühendislik Bilimleri Dergisi 11: 1: 103-114
  • Kaya İ., Oktay,B. ve Engin O. (2005). Yapay Sinir Ağları ile Kalite Kontrol Problemlerinin Çözümü.
  • Koçdar, S. ve Aydın, H. (2013). Açık ve Uzaktan Öğrenme Araştırmalarında Delfi Tekniğinin Kullanımı. Anadolu Üniversitesi Sosyal Bilimler Dergisi Cilt/Vol.: 13 - Sayı/No: 3 (31-44)
  • Li, T. ve Cavusgil, S.T. (1995). A Classification and Assessment of Research Streams in İnternational Marketing. International Business Review, 4(3): 251-77.
  • Lu, X.S., Xie, M., Goh, T.N. ve Lai, C.D. (1998). Control Chart for Multivariate Attribute Processes. International Journal of Production Research. 36:3477-3489.
  • Montgomery, D. C. (1996). Introduction to Statistical Quality Control. New York:Wiley.
  • Mason, R.L., Tracy. N.D. ve Young, J.C. (1997). A Practical Approach for Interpreting Multivariate T2 Control Chart Signals. Journal of Quality Technology. 29:396-406.
  • Masood, I. ve diğerleri (2014). Design of an Artificial Neural Network Pattern Recognition Scheme Using Full Factorial Experiment. Applied Mechanics and Materials Vols. 465-466 1149-1 154
  • Masooud, I. ve Hassan, A. (2010). Issues in Development of Artificial Neural Networkbased Control Chart Pattern Recognition Schemes. European Journal of Scientific Research, 39(3), 336–355.
  • Nelson, L. S. (1984). The Shewhart Control Chart: Test for Special Causes. Journal of Quality Technology, 16(4), 237–239.
  • Nelson, L. S. (1985). Interpreting Shewhart X-Bar Control Charts. Journal of Quality Technology, 17(2), 114–116.
  • Niaki, S.T.A. ve Abbasi, B. (2006). Fault Diagnosis in Multivariate Control Charts Using Artificial Neural Networks. Quality and Reliability Engineering International. 825-840.
  • Niaki, S.T.A. ve Abbasi, B. (2008). Detection and Classification Mean-Shifts in MultiAttribute Processes by Artificial Neural Networks. International Journal of Production Research. 46:2945-2963.
  • Niaki, S.T.A. ve Nasaji, S.A. (2011). A Hybrid Method of Artificial Neural Networks and Simulated Annealing in Monitoring Auto-Correlated Multi-Attribute Processes.
  • International Journal of Advanced Manufacturing Technology. Niaki, S.T.A. ve Abbasi, B. (2005). “Fault Diagnosis in Multivariate Control Charts Using Artificial Neural Networks, Quality and Reliability Engineering International 21, 825-840.
  • Niaki, S.T.A. ve Davoodi, M. (2009). Designing a Multivariate-Multistage Quality Control System Using Artificial Neural Networks. International Journal of Production Research. 47:251-271.
  • Noorossana, R., Atashgar, K. ve Saghaei, A. (2010). An İntegrated Supervised Learning Solution for Monitoring Process Meaén Vector. International Journal of Advanced Manufacturing Technology.
  • Öztemel, E. (2012). Yapay Sinir Ağları, Ezgi Kitapevi, Bursa.
  • Pham, D.T., ve Chan, A.B. (1998). Control Chart Pattern Recognition Using a New Type of Self Organizing Neural Networks, Proc. Instn. Mech. Engrs212 (Part I), 115-127.
  • Pham, D.T. ve Chan, A.B. (1999). A SynergisticSelf-Organizing System for Control Chart Pattern Recognition in S. G. Tzafestas. Advances in Manufacturing: Decision. Control and Information Technology. Springer-Verlag, Londra.
  • Pham, D.T. ve Chan, A.B. (2001). Unsupervised Adaptive Resonance Theory Neural Networks for Control Chart Pattern Recognition. Proc. Instn. Mech. Engrs. 215 (Kısım B), 59-67.
  • Pham, D.T. ve Oztemel, E. (1993). Control Chart Pattern Recognition Using Combinations of Multilayer Perceptrons and Learning Vector Quantization Neural Networks. Proc. Instn. Mech. Engrs. 207, 113-118.
  • Pham, D.T. ve Oztemel, E. (1994). Control Chart Pattern Recognition Using Learning Vector Quantization Networks. International Journal of Production Research 32, 721-729.
  • Pham, D. T. ve Oztemel, E. (1995). An İntegrated Neural Network and Expert System Tool for Statistical Process Control. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of engineering manufacture, 209(2), 91–97.
  • Pham, D.T. ve Wani, M.A. (1997). Feature-Based Control Chart Pattern Recognition. Int J Prod Res 35(7):1875–1890 Pugh, G. A. (1989). Synthetic Neural Networks for Process Control. Computers and Industrial Engineering, 17(1–4), 24–26
  • Roberts, S. W. (1959). Control Chart Tests Based on Geometric Moving Averages. Technometrics, 1(3), 239–244.
  • Stelios, P., (2011). The Use of Neural Networks in Statistical Process Control Charts. Qual. Reliab. Engng. Int. 27 641-650
  • Swift, J. A. (1987). Development of Knowledge Based Expert System for Control Chart Pattern Recognition and Analysis. Unpublished Doctoral Thesis. Stillwater, OK:The Oklahoma State University.
  • Tontini, G. (1998). Robust Learning and Identification of Patterns in Statistical Process Control Charts Using a Hybrid RBF Fuzzy Artmap Neural Network, The 1998 IEEE International Joint Conference on Neural Network Proceedings (IEEE World Congress on Computational Intelligence) 3,1694-1699.
  • Yu, J.B. ve 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, 909-921.
  • Yu, J.B., Xi, L.F. ve Zhou, X., (2013). Identifying Source(s) of Out-of-Control Signals in Multivariate Manufacturing Process Using Selective Neural Network Ensemble. Engineering Applications of Artificial Intelligence,22:141-152.
  • Yuehjen, E. ve Chia-Ding, H., (2013). Hybrid Artificial Neural Networks Modeling for Faults Identification of a Stochastic Multivariate Process” Hindawi Publishing Corporation Abstract and Applied Analysis, (2),2,34-35.
  • Yang, W., (2013). Monitoring and Diagnosing of Mean Shifts in Multivariate Manufacturing Processes Using Two-Level Selective Ensemble of Learning Vector Quantization Neural Networks. Springer Science+Business Media New York.
  • Wang, T.Y. ve Chen, L.H., (2001). Mean Shifts Detection and Classification in Multivariate Process: A Neural-Fuzzy Approach”. Journal of Intelligence Manufacturing. 13:211-221.
  • Wang, C.H., Kuo, W. ve Qi, H. (2007). An İntegrated Approach for Process Monitoring Using Wavelet Analysis and Competitive Neural Network. Int J Prod Res 45:227–244
  • Western Electric Company, (1958). Statistical Quality Control Handbook. Indianapolis, Indiana: Western Electric Co. Inc
  • Zorriassatine, F., Tannock, J.D.T. ve O’Brien, C. (2003). Using Novelty Detection to İdentify Abnormalities Caused by Mean Shifts in Bivariate Processes. Computers and Industrial Engineering;44:385-408.
Sosyal Bilimler Araştırma Dergisi-Cover
  • Yayın Aralığı: Yılda 4 Sayı
  • Yayıncı: Denta Florya ADSM Limited Company
Arşiv
Sayıdaki Diğer Makaleler

Çok Değişkenli Kontrol Kartları Örüntü Tanıma Literatüründe Bir Araştırma

Kenan ORÇANLI, Erkan OKTAY, Burak BİRGÖREN

Güneş Etkinliğinin Demografik Ve Ekonomik Göstergelere Etkisinin Ekonometrik Değerlendirilmesi

Yadulla Hasanli HAMDULLAOGLU, Nizami ISMAYILOV

Yapısal Eşitlik Modellemesi ile Baş Dönmesi, Kaygı ve Bedensel Duyumları Abartma Arasındaki İlişkilerin Analizi

Şule Kaya, Mustafa Aytaç, Nuran Bayram

Gelişmekte Olan Ülkelerde Satın Alma Gücü Paritesinin Uzun Dönem Geçerliliğinin Mevsimsel Birim Kök Testleriyle İncelenmesi

Mehmet OZMEN, Sera SANLI

Ekonomik Büyüme ve Çevresel Kirlilik İlişkisi: Çevresel Kuznets Eğrisi ve Türkiye Örneğ

Emel Nur ALBAYRAK, Atilla GÖKÇE

Crisis and Development of Financial Structure in Turkey After 1980

Havva ARABACI, Fatih ÇAVDAR