Change Point Determination for an Attribute Process Using an Artificial Neural Network-Based Approach

The change point identification has played a vital role in process improvement for an attribute process. This identification is able to effectively help process personnel to quickly determine the corresponding root causes and significantly improve the underlying process. Although many studies have focused on identifying the change point of a process, a generic identification approach has not been developed. The typical maximum likelihood estimator (MLE) approach has limitations: particularly, the known prior process distribution and mathematical difficulties. These deficiencies are commonly encountered in practice. Accordingly, this study proposes an artificial neural network (ANN) mechanism to overcome the difficulties of typical MLE approach in determining the change point of an attribute process. Specifically, the performance among the statistical process control (SPC) chart alone, the typical MLE approach, and the proposed ANN mechanism are investigated for the following cases: (1) a known attribute process distribution with the associated MLE being available to be used, (2) an unknown attribute process distribution with the MLE being unable to be used, and (3) an unknown attribute process distribution with the MLE being misused. The superior results and the performance of the proposed approach are reported and discussed.

[1]  Rassoul Noorossana,et al.  Identifying the period of a step change in high‐yield processes , 2009, Qual. Reliab. Eng. Int..

[2]  D. M. Titterington,et al.  Neural Networks: A Review from a Statistical Perspective , 1994 .

[3]  Joseph J. Pignatiello,et al.  Estimation of the Change Point of a Normal Process Mean in SPC Applications , 2001 .

[4]  Xiaodong Li,et al.  Time series forecasting by evolving artificial neural networks with genetic algorithms, differential evolution and estimation of distribution algorithm , 2011, Neural Computing and Applications.

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

[6]  Chih-Chou Chiu,et al.  Hybrid intelligent modeling schemes for heart disease classification , 2014, Appl. Soft Comput..

[7]  Joseph J. Pignatiello,et al.  IDENTIFYING THE TIME OF A STEP CHANGE IN A NORMAL PROCESS VARIANCE , 1998 .

[8]  Yuehjen E. Shao,et al.  Hybrid Artificial Neural Networks Modeling for Faults Identification of a Stochastic Multivariate Process , 2013 .

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

[10]  Yuehjen E. Shao,et al.  A Combined MLE and Generalized P Chart Approach to Estimate the Change Point of a Multinomial Process , 2013 .

[11]  Rassoul Noorossana,et al.  Using Neural Networks to Detect and Classify Out‐of‐control Signals in Autocorrelated Processes , 2003 .

[12]  Joseph J. Pignatiello,et al.  IDENTIFYING THE TIME OF A STEP-CHANGE IN THE PROCESS FRACTION NONCONFORMING , 2001 .

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

[14]  James C. Benneyan,et al.  Statistical Control Charts Based on a Geometric Distribution , 1992 .

[15]  J. Humberto Pérez-Cruz,et al.  Tracking Control Based on Recurrent Neural Networks for Nonlinear Systems with Multiple Inputs and U , 2012 .

[16]  Jose de Jesus Rubio,et al.  Modified optimal control with a backpropagation network for robotic arms , 2012 .

[17]  Xuemei Ren,et al.  Identification of Extended Hammerstein Systems Using Dynamic Self-Optimizing Neural Networks , 2011, IEEE Transactions on Neural Networks.

[18]  Joseph J. Pignatiello,et al.  IDENTIFYING THE TIME OF A CHANGE IN A POISSON RATE PARAMETER , 1998 .