Control Chart Pattern Recognition Using Wavelet Analysis and Neural Networks

Control charts are useful tool in detecting out-of-control situations in process data. There are many unnatural patterns that may exist in process data indicating the process is out of control. The presence of unnatural patterns implies that a process is affected by assignable causes, and corrective actions should be taken. Identification of unnatural patterns can greatly narrow the set of possible causes that must be investigated, and thus the diagnostic work could be reduced in length. This paper presents a modified self-organizing neural network developed for control chart pattern analysis. The aim is to develop a pattern clustering approach when no prior knowledge of the unnatural patterns is available. This paper also investigates the use of features extracted from wavelet analysis as the components of the input vectors. Experimental results and comparisons based on simulated and real data show that the proposed approach performs better than traditional approach. Our research concluded that the extracted features can improve the performance of the proposed neural network.

[1]  Hui Ping Cheng,et al.  Denoising and feature extraction for control chart pattern recognition in autocorrelated processes , 2008 .

[2]  Shankar Chakraborty,et al.  Recognition of control chart patterns using improved selection of features , 2009, Comput. Ind. Eng..

[3]  Ercan Oztemel,et al.  Control chart pattern recognition using neural networks , 1992 .

[4]  Teuvo Kohonen,et al.  Self-Organization and Associative Memory , 1988 .

[5]  Chuen-Sheng Cheng,et al.  A NEURAL NETWORK APPROACH FOR THE ANALYSIS OF CONTROL CHART PATTERNS , 1997 .

[6]  A. Anglani,et al.  Adaptive resonance theory-based neural algorithms for manufacturing process quality control , 2004 .

[7]  Kai Yang,et al.  Application of artificial neural network to identify non-random variation patterns on the run chart in automotive assembly process , 2003 .

[8]  Chuen-Sheng Cheng,et al.  A pattern recognition algorithm for an x¯ control chart , 1996 .

[9]  Yi-Chih Hsieh,et al.  A neural network based model for abnormal pattern recognition of control charts , 1999 .

[10]  Miin-Shen Yang,et al.  A control chart pattern recognition system using a statistical correlation coefficient method , 2005, Comput. Ind. Eng..

[11]  Norma Faris Hubele,et al.  Back-propagation pattern recognizers for X¯ control charts: methodology and performance , 1993 .

[12]  N F Hubele,et al.  X¯ CONTROL CHART PATTERN IDENTIFICATION THROUGH EFFICIENT OFF-LINE NEURAL NETWORK TRAINING , 1993 .

[13]  Alice E. Smith,et al.  X-bar and R control chart interpretation using neural computing , 1994 .

[14]  Hairong Qi,et al.  An integrated approach for process monitoring using wavelet analysis and competitive neural network , 2007 .

[15]  Duc Truong Pham,et al.  Unsupervised adaptive resonance theory neural networks for control chart pattern recognition , 2001 .

[16]  Duc Truong Pham,et al.  Control chart pattern recognition using learning vector quantization networks , 1994 .

[17]  N. C. Nigam.  Introduction to Random Vibrations , 1983 .

[18]  Yousef Al-Assaf,et al.  Recognition of control chart patterns using multi-resolution wavelets analysis and neural networks , 2004, Comput. Ind. Eng..

[19]  Duc Truong Pham,et al.  Feature-based control chart pattern recognition , 1997 .

[20]  J.D.T. Tannock,et al.  Recognition of control chart concurrent patterns using a neural network approach , 1999 .

[21]  Marcus B. Perry,et al.  Control chart pattern recognition using back propagation artificial neural networks , 2001 .

[22]  C.-S. Cheng.  Group technology and expert systems concepts applied to statistical process control in small-batch manufacturing , 1989 .

[23]  Adnan Hassan,et al.  Improved SPC chart pattern recognition using statistical features , 2003 .

[24]  Duc Truong Pham,et al.  Efficient control chart pattern recognition through synergistic and distributed artificial neural networks , 1999 .

[25]  Miin-Shen Yang,et al.  A fuzzy-soft learning vector quantization for control chart pattern recognition , 2002 .

[26]  Duc Truong Pham,et al.  Control chart pattern recognition using a new type of self-organizing neural network , 1998 .