Pattern recognition for bivariate process mean shifts using feature-based artificial neural network

In multivariate quality control, the artificial neural networks (ANN)-based pattern recognition schemes generally performed better for monitoring bivariate process mean shifts and provided more efficient information for diagnosing the source variable(s) compared to the traditional multivariate statistical process control charting. However, these schemes revealed disadvantages in term of reference bivariate patterns in identifying the joint effect and excess false alarms in identifying stable process condition. In this study, feature-based ANN scheme was investigated for recognizing bivariate correlated patterns. Feature-based input representation was utilized into an ANN training and testing towards strengthening discrimination capability between bivariate normal and bivariate mean shift patterns. Besides indicating an effective diagnosis capability in dealing with low correlation bivariate patterns, the proposed scheme promotes a smaller network size and better monitoring capability as compared to the raw data-based ANN scheme.

[1]  Cheng-Wu Chen,et al.  Modeling and control for nonlinear structural systems via a NN-based approach , 2009, Expert Syst. Appl..

[2]  Stelios Psarakis,et al.  Multivariate statistical process control charts: an overview , 2007, Qual. Reliab. Eng. Int..

[3]  Chin-Teng Lin,et al.  A Novel Two-Stage Impulse Noise Removal Technique Based on Neural Networks and Fuzzy Decision , 2008, IEEE Transactions on Fuzzy Systems.

[4]  Lloyd S. Nelson,et al.  Standardization of Shewhart Control Charts , 1989 .

[5]  Kandarpa Kumar Sarma,et al.  ANN-based Innovative Segmentation Method for Handwritten text in Assamese , 2009, ArXiv.

[6]  Cheng-Wu Chen,et al.  Fuzzy Control for an Oceanic Structure: A Case Study in Time-delay TLP System , 2010 .

[7]  Tomas Velasco,et al.  Back propagation artificial neural networks for the analysis of quality control charts , 1993 .

[8]  F. Zorriassatine,et al.  Using novelty detection to identify abnormalities caused by mean shifts in bivariate processes , 2003 .

[9]  Yoav Benjamini,et al.  Multivariate Profile Charts for Statistical Process Control , 1994 .

[10]  Tai-Yue Wang,et al.  Artificial neural networks to classify mean shifts from multivariate χ2 chart signals , 2004, Comput. Ind. Eng..

[11]  Ruey-Shiang Guh,et al.  On‐line Identification and Quantification of Mean Shifts in Bivariate Processes using a Neural Network‐based Approach , 2007, Qual. Reliab. Eng. Int..

[12]  W. A. Wallis,et al.  Techniques of Statistical Analysis. , 1950 .

[13]  Lifeng Xi,et al.  A neural network ensemble-based model for on-line monitoring and diagnosis of out-of-control signals in multivariate manufacturing processes , 2009, Expert Syst. Appl..

[14]  Ronald B. Crosier,et al.  A new two-sided cumulative sum quality control scheme , 1986 .

[15]  Sadik Kara,et al.  Diagnosis of the macular diseases from pattern electroretinography signals using artificial neural networks , 2006, Expert Syst. Appl..

[16]  Rahul Singh,et al.  A real-time information system for multivariate statistical process control , 2002 .

[17]  John S. Oakland,et al.  Statistical Process Control , 2018 .

[18]  Kwang-Hyun Cho,et al.  Run-to-run overlay control of steppers in semiconductor manufacturing systems based on history data analysis and neural network modeling , 2005 .

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

[20]  Zheng Chen,et al.  A hybrid system for SPC concurrent pattern recognition , 2007, Adv. Eng. Informatics.

[21]  R. Lehman.  Computer simulation and modeling : an introduction , 1978 .

[22]  A. R. Crathorne,et al.  Economic Control of Quality of Manufactured Product. , 1933 .

[23]  J. D. T. Tannock,et al.  On-line control chart pattern detection and discrimination - a neural network approach , 1999, Artif. Intell. Eng..

[24]  Seyed Taghi Akhavan Niaki,et al.  Fault Diagnosis in Multivariate Control Charts Using Artificial Neural Networks , 2005 .

[25]  Ibrahim Masood,et al.  Issues in development of artificial neural network-based control chart pattern recognition schemes , 2010 .

[26]  Duc Truong Pham,et al.  Control Chart Pattern Recognition Using Combinations of Multi-Layer Perceptrons and Learning-Vector-Quantization Neural Networks , 1993 .

[27]  Xiaojun Zhou,et al.  Identifying source(s) of out-of-control signals in multivariate manufacturing processes using selective neural network ensemble , 2009, Eng. Appl. Artif. Intell..

[28]  Christopher M. Bishop,et al.  Neural Network for Pattern Recognition , 1995 .

[29]  Zhenyuan Jia,et al.  Characteristics forecasting of hydraulic valve based on grey correlation and ANFIS , 2010, Expert Syst. Appl..

[30]  Veerendra Singh,et al.  Developing a machine vision system for spangle classification using image processing and artificial neural network , 2006 .

[31]  Douglas C. Montgomery,et al.  A review of multivariate control charts , 1995 .

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

[33]  Jonny Eriksson,et al.  Feature reduction for classification of multidimensional data , 2000, Pattern Recognit..

[34]  Ibrahim N. Tansel,et al.  Fault diagnosis of pneumatic systems with artificial neural network algorithms , 2009, Expert Syst. Appl..

[35]  James M. Lucas,et al.  Exponentially weighted moving average control schemes: Properties and enhancements , 1990 .

[36]  Khaled Assaleh,et al.  Features extraction and analysis for classifying causable patterns in control charts , 2005, Comput. Ind. Eng..

[37]  Adnan Amin,et al.  Recognition of printed arabic text based on global features and decision tree learning techniques , 2000, Pattern Recognit..

[38]  John C. Young,et al.  Monitoring a multivariate step process , 1996 .

[39]  Mark Beale,et al.  Neural Network Toolbox™ User's Guide , 2015 .

[40]  C.I. Christodoulou,et al.  Unsupervised pattern recognition for the classification of EMG signals , 1999, IEEE Transactions on Biomedical Engineering.

[41]  Charles W. Champ,et al.  A multivariate exponentially weighted moving average control chart , 1992 .

[42]  Tawfik T. El-Midany,et al.  A proposed framework for control chart pattern recognition in multivariate process using artificial neural networks , 2010, Expert Syst. Appl..

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

[44]  Douglas C. Montgomery,et al.  Introduction to Statistical Quality Control , 1986 .

[45]  Cheng-Wu Chen,et al.  Adaptive Fuzzy Sliding Mode Control for Seismically excited Bridges with lead Rubber Bearing Isolation , 2009, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

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

[47]  Chuen-Sheng Cheng.  A multi-layer neural network model for detecting changes in the process mean , 1995 .

[48]  Osman Erogul,et al.  A new approach to urinary system dynamics problems: Evaluation and classification of uroflowmeter signals using artificial neural networks , 2009, Expert Syst. Appl..

[49]  Geoffrey Vining.  Multivariate Quality Control Procedures A. J. Hay ter , 1999 .

[50]  Pilar Rodriguez-Loaiza,et al.  Application of the Multivariate T2 Control Chart and the Mason–Tracy–Young Decomposition Procedure to the Study of the Consistency of Impurity Profiles of Drug Substances , 2003 .

[51]  J. D. T. Tannock,et al.  A review of neural networks for statistical process control , 1998, J. Intell. Manuf..

[52]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

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

[54]  J. Edward Jackson,et al.  A User's Guide to Principal Components: Jackson/User's Guide to Principal Components , 2004 .

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

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

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

[58]  Theodora Kourti,et al.  Multivariate SPC Methods for Process and Product Monitoring , 1996 .

[59]  Chuen-Sheng Cheng,et al.  Identifying the source of variance shifts in the multivariate process using neural networks and support vector machines , 2008, Expert Syst. Appl..

[60]  Joel A. Nachlas,et al.  A simulation approach to multivariate quality control , 1997 .

[61]  Teodor Marcu,et al.  Design of fault detection for a hydraulic looper using dynamic neural networks , 2008 .

[62]  John C. Young,et al.  A Practical Approach for Interpreting Multivariate T2 Control Chart Signals , 1997 .

[63]  R. Crosier.  Multivariate generalizations of cumulative sum quality-control schemes , 1988 .

[64]  Shankar Chakraborty,et al.  Feature-based recognition of control chart patterns , 2006, Comput. Ind. Eng..

[65]  R Guh.  IntelliSPC: a hybrid intelligent tool for on-line economical statistical process control , 1999 .

[66]  Shankar Chakraborty,et al.  Improved recognition of control chart patterns using artificial neural networks , 2008 .

[67]  George C. Runger,et al.  Designing a Multivariate EWMA Control Chart , 1997 .

[68]  Jan M. Zytkow,et al.  Handbook of Data Mining and Knowledge Discovery , 2002 .

[69]  Christian Wöhler,et al.  PII: S0262-8856(98)00108-5 , 1999 .

[70]  Fevzullah Temurtas,et al.  Chest diseases diagnosis using artificial neural networks , 2010, Expert Syst. Appl..

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

[72]  Youn Min Chou,et al.  Applying Hotelling's T2 Statistic to Batch Processes , 2001 .

[73]  George C. Runger,et al.  Comparison of multivariate CUSUM charts , 1990 .

[74]  Nola D. Tracy,et al.  Decomposition of T2 for Multivariate Control Chart Interpretation , 1995 .

[75]  Frank B. Alt.  Multivariate Quality Control , 1984 .

[76]  Michael S. Dudzic,et al.  An industrial perspective on implementing on-line applications of multivariate statistics , 2004 .

[77]  Ruey-Shiang Guh,et al.  Simultaneous process mean and variance monitoring using artificial neural networks , 2010, Comput. Ind. Eng..