A Framework for Diagnosing the Out-of-Control Signals in Multivariate Process Using Optimized Support Vector Machines

Tai-Fu Li,Zhi-Qiang Liao,Zheng-Yuan Wei,Sheng Hu

doi:10.1155/2013/494626

Abstract

Multivariate statistical process control is the continuation and development of unitary statistical process control. Most multivariate statistical quality control charts are usually used (in manufacturing and service industries) to determine whether a process is performing as intended or if there are some unnatural causes of variation upon an overall statistics. Once the control chart detects out-of-control signals, one difficulty encountered with multivariate control charts is the interpretation of an out-of-control signal. That is, we have to determine whether one or more or a combination of variables is responsible for the abnormal signal. A novel approach for diagnosing the out-of-control signals in the multivariate process is described in this paper. The proposed methodology uses the optimized support vector machines (support vector machine classification based on genetic algorithm) to recognize set of subclasses of multivariate abnormal patters, identify the responsible variable(s) on the occurrence of abnormal pattern. Multiple sets of experiments are used to verify this model. The performance of the proposed approach demonstrates that this model can accurately classify the source(s) of out-of-control signal and even outperforms the conventional multivariate control scheme.

Highlights

Statistical process control (SPC) is one of the most effective tools in total quality management (TQM), which is used to monitor manufacture process variation
According to the state of control chart, we can be informed whether the manufacture process is in in-control state or not; quality practitioners or engineers search for the assignable causes and take some necessary corrections and adjustments to bring the out-ofcontrol process back to the in-control state [3]
In this study, considering the robust recognition power of support vector machine and the global search capability of the genetic algorithm, an optimized SVM approach named GA-SVM was developed; a framework of control chart pattern recognition of multivariate observation data is proposed for classifying source(s) of out-of-control signals in multivariate processes

Summary

Introduction

Statistical process control (SPC) is one of the most effective tools in total quality management (TQM), which is used to monitor manufacture process variation. Neural networks (NNs) have excellent noise tolerance in real time, requiring no hypothesis on statistical distribution of monitored measurements These important features make NNs promising and effective tools that can be used to improve data analysis in manufacturing quality control applications. In this study, considering the robust recognition power of support vector machine and the global search capability of the genetic algorithm, an optimized SVM approach named GA-SVM (support vector machine classification based on genetic algorithms) was developed; a framework of control chart pattern recognition of multivariate observation data is proposed for classifying source(s) of out-of-control signals in multivariate processes.

Methodology

Diagnosis Model Based on Optimized Support Vector Machine in the Process

Simulation and Analysis

Test 1

Test 2

Conclusion and Further Work