Identification of various control chart patterns using support vector machine and wavelet analysis

R. Russo, G. Romano *, P. Colombo

Department of Computer Services and Telecommunications Area (ASIT), Ca' Foscari University of Venice, Dorsoduro 3246, 30123 Venice, Italy

Abstract

The monitoring of production process is the basis of high-quality production. The control chart patterns (CCP) tool is one of the most efficient methods in production process monitoring. The control chart patterns are consisting of six different patterns. Each pattern indicates a special problem in the production process. The only normal pattern indicates the normal condition in the factory. The identification of these six different and independent patterns is a complicated and nonlinear classification problem. In this study, a smart system is proposed to classify these patterns by high accuracy. The proposed system uses a support vector machine (SVM) as a classifier. In support vector machine the type of kernel function and other parameters have a high effect on its performance. There are no systematic methods to determine these parameters. In the proposed method bee’s algorithm is suggested to determine these parameters. Also in the pattern recognition field, the input data has a high effect on classification accuracy. In the proposed method, wavelet analysis is proposed as a feature selection section. In this section, approximation coefficients are selected as efficient input to support vector machine. To test the performance of the proposed method, the real database is used. The computer simulation results show that the proposed hybrid system has excellent performance and recognition accuracy.

Keywords

Wavelet, Optimization, Bee’s algorithm, Pattern, Support vector machine

Digital Object Identifier (DOI)

https://doi.org/10.21833/AEEE.2019.08.002

Article history

Received 2 March 2019, Received in revised form 2 July 2019, Accepted 5 July 2019

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How to cite

Russo R, Romano G, and Colombo P (2019). Identification of various control chart patterns using support vector machine and wavelet analysis. Annals of Electrical and Electronic Engineering, 2(8): 6-12

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