Automated Identification of Basic Control Charts Patterns Using Neural Networks

Ahmed Shaban,Ehab Abdelhafiez,Ashraf S Youssef,Mohammed Shalaby

doi:10.4236/jsea.2010.33026

Ahmed Shaban, Ehab Abdelhafiez + Show 2 more

Open Access

https://doi.org/10.4236/jsea.2010.33026

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Abstract

The identification of control chart patterns is very important in statistical process control. Control chart patterns are categorized as natural and unnatural. The presence of unnatural patterns means that a process is out of statistical control and there are assignable causes for process variation that should be investigated. This paper proposes an artificial neural network algorithm to identify the three basic control chart patterns; natural, shift, and trend. This identification is in addition to the traditional statistical detection of runs in data, since runs are one of the out of control situations. It is assumed that a process starts as a natural pattern and then may undergo only one out of control pattern at a time. The performance of the proposed algorithm was evaluated by measuring the probability of success in identifying the three basic patterns accurately, and comparing these results with previous research work. The comparison showed that the proposed algorithm realized better identification than others.

Highlights

With the widespread usage of automatic data acquisition system for computer charting and analysis of manufacturing process data, there is a need to automate the analysis of process data with little or no human intervention [1]
Artificial Neural Network (ANN) models are expected to overcome the problem of high false alarm rate; because it does not depend on any statistical tests that are usually required for the traditional methods
Al-assaf [32] used the probability of success to compare between three approaches (DC, MRWA, and MRWA + DSC) to detect and classify the control chart unnatural patterns, his best results was obtained by using MRWA + DSC, so these results was used in the comparison

Summary

Introduction

With the widespread usage of automatic data acquisition system for computer charting and analysis of manufacturing process data, there is a need to automate the analysis of process data with little or no human intervention [1]. Traditional control charts use only recent sample data point to determine the status of the process based on the control limits only. They do not provide any pattern related information. This paper proposes an Artificial Neural Network algorithm to detect and identify the three basic control chart patterns; natural, shift, and trend. Natural variation is represented by normal (0, 1) variation, shift in process mean is expressed in terms of number of standard deviations and trend is expressed as the general slope of a trend line This identification of each pattern is in addition to the traditional statistical detection of data runs. The paper presents a literature review, the design of the ANN network, the proposed approach for ANN, testing of the ANN algorithm and the performance evaluation of the algorithm

Artificial Neural Network Approaches

Basic Neural Network Design

Network Training Data Generation

Network Training Process

Network Testing and Performance Evaluation

Conclusions