Design Methodology of Modular-Ann Pattern Recognizer for Bivariate Quality Control

Nurul Adlihisam Mohd Sohaimi, Ibrahim Masood, Musli Mohammad, Mohd Fahrul Hassan


In quality control, monitoring unnatural variation (UV) in manufacturing process has become more challenging when dealing with two correlated variables (bivariate). The traditional multivariate statistical process control (MSPC) charts are only effective for triggering UV but unable to provide information towards diagnosis. In recent years, a branch of research has been focused on control chart pattern recognition (CCPR) technique. However, findings on the source of UV are still limited to sudden shifts patterns. In this study, a methodology to develop a CCPR scheme was proposed to identify various sources of UV based on shifts, trends, and cyclic patterns. The success factor for the scheme was outlined as a guideline for realizing accurate monitoring-diagnosis in bivariate quality control.


Bivariate quality control; Control chart pattern recognition; Modular neural network; Unnatural variation;

Full Text:



Zorriassatine, F., Tannock, J.D.T (1998). A Reviewof Natural Network for Statistical Process Control, Journal Intelligent Manufacturing 9, pp.209-224.

Haykin, S. (1999) Neural Network: A Comprehensive Foundation, Prentice-Hall, Englewood Cliffs, NJ.

Schalkoff, R.J., 1997. Artificial neural network, McGraw-Hill, New York.

Haykin, S. (1999) Neural Network: A Comprehensive Foundation, 2nd ed., Prentise Hall, New Jersey.

Bag, M. and Gauri, S.K. (2012). An Expert System for Control Chart Pattern Recognition. International Journal Advanced Manufacturing Technology, pp. 291 – 301.

Chen,L.H., Wang, T.Y.,2004. Artificial Neural Network to Classify Mean Shift From Multivariate X2 Chart Signals. Computer and Industrial Engineering 28, pp 195-205.

Yu, J.B., and Xi, L.F., 2009. A Neural Network Ensemble-Based Model for On-Line Monitoring and Diagnosis of Out-of-Control Signal in Multivariate Manufacturing Processes, Expert System with Applications 36, pp. 909-921.

Guh, R.S. and Tannock, J.D.T. (1999). A Neural Network Approach to Characterize Pattern Parameters in Process Control Charts. Journal of Intelligent Manufacturing 10, pp.449 – 462

Masood I, Hassan A (2010) Issue in development of artificial neural network-based control chart pattern recognition schemes. Eur J Sci Res 39(3); 336-355.

Cheng, C.s., 1997. A Neural Network Approach for the Analysis of Control Chart Pattern, International Journal of Production Research 35, pp. 667-697.

Gauri, S.K. and Chakraborty, S. (2008). Improve Recognition of Control Chart Patterns Using Artificial Neural Network. International journal of Advanced Manufacturing Technology, pp.1191-1201.

Pham, D.T., and Oztemel, E., 1993. Control Chart Pattern Recognition Using Combinations of Multilayer Perceptrons and Learning Vectro Quantization Neural Networks, Proc. Instn. Mech. Engrs. 207, pp. 113-118.

Zorriassatine, F., Tannock, J.D.T. and O’Brien, C. (2003). Using Novelty Detection to Identify Abnormalities Caused by Mean Shifts in Bivariate Processes. Computer and Industrial Engineering, pp.385 – 408

Niaki, S.T.A and Abbasi, B. (2005). Fault Diagnosi in Multivariate Control Chart Using Artificial Neural Networks. Quality and Realibility Engineering International, pp. 825-840.

Pham, D.T and Wani, M.A., 1997, Features-based controls chart pattern recognition. International Journal of Production Research, 35, 1875-1890.

Gauri, S.K. and Chakraborty, S. (2006). Feature-Based Recognition of Control Chart Patterns. Computer and Industrial Engineering, pp.726 – 742.

Masood, I. and Hassan, A. (2014). Bivariate Quality Control Using Two-Stage Intelligent Monitoring Scheme.Expert Systems with Applications 41, pp.7579 - 7595.


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.

ISSN: 2180-1843

eISSN: 2289-8131