Research Article | OPEN ACCESS
Abnormal Control Chart Pattern Classification Optimisation Using Multi Layered Perceptron
Mitra Mahdiani, Hairulliza Mohd. Judi and Noraidah Sahari Ashaari
Department of Industrial Computing, Faculty of Information Science and Technology,
Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia
Research Journal of Applied Sciences, Engineering and Technology 2014 22:4690-4695
Received: October 22, 2013 | Accepted: November 16, 2013 | Published: June 10, 2014
Abstract
In today's industry, control charts are widely used to monitor production process. The abnormal patterns of a quality control chart could reveal problems that occur in the process. In the recent years, as an alternative of the traditional process quality management methods, such as Shewhart Statistical Process Control (SPC), Artificial Neural Networks (ANN) have been widely used to recognize the abnormal pattern of control charts. Various types of patterns are observed in control charts. Identification of these Control Chart Patterns (CCPs) can provide clues to potential quality problems in the manufacturing process. Each type of control chart pattern has its own geometric shape and various related features can represent this shape. Feature-based approaches can facilitate efficient pattern recognition since extracted shape features represent the main characteristics of the patterns in a condensed form. The objective of this study was to evaluate the relative performance of a feature-based CCP recognizer compared with the raw data-based recognizer. The study focused on recognition of six commonly researched CCPs plotted on the Shewhart X-bar chart. The ANN-based CCP recognizer trained using the nine shape features resulted in significantly better performance and generalization compared with the raw data-based recognizer.
Keywords:
Artificial neural networks, control chart patterns, shape features, statistical process control,
References
-
Addeh, J., A. Ebrahimzadeh and V. Ranaee, 2011a. Application of the PSO-RBFNN model for recognition of control chart patterns. Proceeding of the 2011 2nd International Conference IEEE Conference Control, Instrumentation and Automation (ICCIA), pp: 742-746.
CrossRef
-
Addeh, J., A. Ebrahimzadeh and V. Ranaee, 2011b. Control chart pattern recognition using adaptive back-propagation artificial neural networks and efficient features. Proceeding of the 2011 2nd IEEE International Conference on Control, Instrumentation and Automation (ICCIA), pp: 747-752.
CrossRef
-
Battiti, R., 1992. First and second order methods for learning: Between steepest descent and Newton's method. Neural Comput., 4(2): 141-166.
CrossRef
-
Campbell, C. and N. Cristianini, 1998. Simple learning algorithms for training support vector machines. Technical Report, University of Bristol.
-
Cheng, Z. and Y. Ma, 2008. A research about pattern recognition of control chart using probability neural network. Proceeding of the ISECS International Colloquium on Computing, Communication, Control and Management, 2: 140-145.
CrossRef
-
Ebrahimzadeh, A. and V. Ranaee, 2010. Control chart pattern recognition using an optimized neural network and efficient features. ISA T., 49(3): 387-393.
CrossRef PMid:20403598
-
Ebrahimzadeh, A., J. Addeh and Z. Rahmani, 2012. Control chart pattern recognition using K-MICA clustering and neural networks. ISA T., 51(1): 111-119.
CrossRef PMid:22035774
-
Evans, J.R. and W.M. Lindsay, 1988. A framework for expert system development in statistical quality control. Comput. Ind. Eng., 14(3): 335-343.
CrossRef
-
Gauri, S.K. and S. Chakraborty, 2009. Recognition of control chart patterns using improved selection of features. Comput. Ind. Eng., 56(4): 1577-1588.
CrossRef
-
Hagan, M.T. and M.B. Menhaj, 1994. Training feed-forward networks with the Marquardt algorithm. IEEE T. Neural Networ., 5(6): 989- 993.
CrossRef PMid:18267874
-
Hassan, A., M.S.N. Baksh, A.M. Shaharoun and H. Jamaluddin, 2003. Improved SPC chart pattern recognition using statistical features. Int. J. Prod. Res., 41(7): 1587-1603.
CrossRef
-
Haykin, S.S., 1999. Neural Network: A Comprehensive Foundation. 2nd Edn., Prentice Hall, Upper Saddle River, NJ, pp: 842.
-
Le, Q., X. Goal, L. Teng and M. Zhu, 2004. A new ANN model and its application in pattern recognition of control charts. Proceeding of the 5th World Congress on Intelligent Control and Automation (WCICA, 2004), 2: 1807-1811.
-
Moller, M., 1990. A scaled conjugate gradient algorithm for fast supervised learning. Computer Science Department, Aarhus University, Vol. 339, DAIMI PB, ISSN: 0105-8517.
-
Montgomery, D.C., 2008. Introduction to Statistical Quality Control. 6th Edn., Technology and Engineering. John Wiley and Sons Inc., Hoboken, NJ., USA.
-
Pacella, M., Q. Semeraro and A. Anglani, 2004. Adaptive resonance theory-based neural algorithms for manufacturing process quality control. Int. J. Prod. Res., 42(21): 4581-4607.
CrossRef
-
Pham, D.T. and E. Oztemel, 1992. Control chart pattern recognition using neural networks. J. Syst. Eng., 2(4): 256-62.
-
Pham, D.T. and E. Oztemel, 1994. Control chart pattern recognition using linear vector quantization networks. Int. J. Prod. Res., 23: 256-262.
-
Pham, D.T. and M.A. Wani, 1997. Feature-based control chart pattern recognition. Int. J. Prod. Res., 35(7): 1875-1890.
CrossRef
-
Ranaee, V. and A. Ebrahimzadeh, 2011. Control chart pattern recognition using a novel hybrid intelligent method. Appl. Soft Comput., 11(2): 2676-2686.
CrossRef
-
Ranaee, V., A. Ebrahimzadeh and R. Ghaderi, 2010. Application of the PSO-SVM model for recognition of control chart patterns. ISA T., 49(4): 577-586.
CrossRef PMid:20663504
-
Riedmiller, M. and H. Braun, 1993. A direct adaptive method for faster back-propagation learning: the rprop algorithm. Proceeding of the IEEE International Conference on Neural Networks (ICNN, 1993), 1: 586-591.
PMid:8256411
-
Sagiroujlu, S., E. Besdoc and M. Erler, 2000. Control chart pattern recognition using artificial neural networks. Turk. J. Electr. Eng. Co., 8(2): 137-146.
-
Saravanan, A. and P. Nagarajan, 2013. Performance of ANN in pattern recognition for process improvement using levenberg-marquardt and quasi-newton algorithms. IOSR J. Eng., 3(3): 08-13.
-
Scales, L.E., 1985. Introduction to Non-linear Optimization. Springer-Verlag, New York.
CrossRef PMCid:PMC424204
-
Swift, J.A. and J.H. Mize, 1995. Out-of-control pattern recognition and analysis for quality control charts using lisp-based systems. Comput. Ind. Eng., 28(1): 81-91.
CrossRef
-
The Numerical Algorithms Group, 2012. Keyword Index: Quasi-Newton. NAG Library Manual, Mark 23. The Numerical Algorithms Group Ltd., Oxford, UK.
-
Wang, C.H., W. Kuo and H. Qi, 2007. An integrated approach for process monitoring using wavelet analysis and competitive neural network. Int. J. Prod. Res., 45(1): 227-244.
CrossRef
-
Wani, M.A. and S. Rashid, 2005. Parallel algorithm for control chart pattern recognition. Proceedings of the 4th International Conference on Machine Learning and Applications (ICMLA '05). IEEE Computer Society, Washington, DC, USA, pp: 89-96.
CrossRef
Competing interests
The authors have no competing interests.
Open Access Policy
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Copyright
The authors have no competing interests.
|
|
![](http://www.maxwellsci.com/images/RJASET-maxw.jpg) |
ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
![Submit Manuscript](../images/sub.jpg) |
Information |
|
|
|
Sales & Services |
|
|
|