Estimating the time of a step change in the multivariate-attribute process mean using ANN and MLE

In this paper, we consider correlated multivariate-attribute quality characteristics and provide two methods including a modular method based on artificial neural network (ANN) as well as maximum likelihood estimation (MLE) method to estimate the time of change in the parameters of the process mean. We evaluate the performance of the estimators in terms of some criteria in change point estimation and compare them through simulation studies. The results show that the proposed ANN-based model outperforms the MLE approach under most step shifts in the mean vector of the multivariate-attribute process.

[1]  Amirhossein Amiri,et al.  Change Point Estimation Methods for Control Chart Postsignal Diagnostics: A Literature Review , 2012, Qual. Reliab. Eng. Int..

[2]  Jan Lundberg,et al.  Multivariate process parameter change identification by neural network , 2013, The International Journal of Advanced Manufacturing Technology.

[3]  Ipek Deveci Kocakoç,et al.  A Multivariate Change Point Detection Procedure for Monitoring Mean and Covariance Simultaneously , 2013, Commun. Stat. Simul. Comput..

[4]  Amirhossein Amiri,et al.  Monitoring multivariate–attribute processes based on transformation techniques , 2013 .

[5]  M. R. Maleki,et al.  MONITORING MULTIVARIATE-ATTRIBUTE PROCESSES USING ARTIFICIAL NEURAL NETWORK , 2012 .

[6]  S. Meysam Mousavi,et al.  A new neural network-based control scheme for fault detection and fault diagnosis in fuzzy multivariate multinomial data , 2015, Int. J. Appl. Decis. Sci..

[7]  Rassoul Noorossana,et al.  Change Point Estimation of Multivariate Linear Profiles Under Linear Drift , 2015, Commun. Stat. Simul. Comput..

[8]  Mohammad Rezazadeh Niavarani,et al.  Multi-variate-attribute quality control (MVAQC) , 2014 .

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

[10]  Seyed Taghi Akhavan Niaki,et al.  Detection and classification mean-shifts in multi-attribute processes by artificial neural networks , 2008 .

[11]  Amirhossein Amiri,et al.  Monitoring correlated variable and attribute quality characteristics based on NORTA inverse technique , 2014 .

[12]  S. M. Mousavi,et al.  Step change point estimation in the multivariate-attribute process variability using artificial neural networks and maximum likelihood estimation , 2015 .

[13]  Mohammad Reza Maleki,et al.  Online Monitoring and Fault Diagnosis of Multivariate-attribute Process Mean Using Neural Networks and Discriminant Analysis Technique , 2015 .

[14]  Joseph J. Pignatiello,et al.  IDENTIFYING THE TIME OF A STEP-CHANGE WITH X 2 CONTROL CHARTS , 1998 .

[15]  Seyed Taghi Akhavan Niaki,et al.  Bootstrap method approach in designing multi-attribute control charts , 2007 .

[16]  Mohammad Hossein Fazel Zarandi,et al.  A general fuzzy-statistical clustering approach for estimating the time of change in variable sampling control charts , 2010, Inf. Sci..

[17]  B. Abbasi,et al.  Generating correlation matrices for normal random vectors in NORTA algorithm using artificial neural networks , 2008 .

[18]  Yuehjen E. Shao,et al.  Change point determination for a multivariate process using a two-stage hybrid scheme , 2013, Appl. Soft Comput..

[19]  R Nour Alsana,et al.  IDENTIFYING CHANGE POINT IN A BIVARIATE NORMAL PROCESS MEAN VECTOR WITH MONOTONIC CHANGES , 2010 .

[20]  Amiri Amir Hossein,et al.  MONITORING VARIABILITY OF MULTIVARIATE-ATTRIBUTE PROCESSES USING ARTIFICIAL NEURAL NETWORK , 2014 .

[21]  Rassoul Noorossana,et al.  Diagnosing the source(s) of a monotonic change in the process mean vector , 2012 .

[22]  Douglas M. Hawkins,et al.  A Multivariate Change-Point Model for Statistical Process Control , 2006, Technometrics.

[23]  Amirhossein Amiri,et al.  Simultaneous Monitoring of Multivariate-Attribute Process Mean and Variability Using Artificial Neural Networks , 2015 .

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

[25]  David S. Matteson,et al.  A Nonparametric Approach for Multiple Change Point Analysis of Multivariate Data , 2013, 1306.4933.

[26]  George C. Runger,et al.  Supervised learning for change-point detection , 2006 .

[27]  F. Ahmadzade,et al.  Identifying the time of a step change with MEWMA control charts by artificial neural network , 2008, 2008 IEEE International Conference on Industrial Engineering and Engineering Management.

[28]  E. Luciano,et al.  Copula methods in finance , 2004 .

[29]  Seyed Taghi Akhavan Niaki,et al.  Skewness Reduction Approach in Multi-Attribute Process Monitoring , 2007 .

[30]  S. Niaki,et al.  Step change-point estimation of multivariate binomial processes , 2014 .

[31]  Ipek Deveci Kocakoç,et al.  Estimation of Change Point in Generalized Variance Control Chart , 2011, Commun. Stat. Simul. Comput..