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All Journal Indonesian Journal of Electrical Engineering and Computer Science
Nor Hasrul Akhmal Ngadiman
Universiti Teknologi Malaysia

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An improved features selection approach for control chart patterns recognition Waseem Alwan; Nor Hasrul Akhmal Ngadiman; Adnan Hassan; Mohd Syahril Ramadhan Mohd Saufi; Azanizawati Ma’aram; Ibrahim Masood
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 2: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i2.pp734-746

Abstract

Control chart patterns (CCPs) are an essential diagnostic tool for process monitoring using statistical process control (SPC). CCPs are widely used to improve production quality in many engineering applications. The principle is to recognize the state of a process, either a stable process or a deterioration to an unstable process. It is used to significantly narrow the set of possible assignable causes by shortening the diagnostic process to improve the quality. Machine learning techniques have been widely used in CCPs. Artificial neural networks with multilayer perceptron (ANN-MLP) are one of the standard tools used for this purpose. This paper proposes an improved features selection method to select the best features as input representation for control chart patterns recognition. The results demonstrate that the proposed approach can effectively recognize CCPs even for small patterns with a mean shift of less than 1.5 sigma. The dimensional reduction was achieved by employing Relief, correlation, and Fisher algorithms (RCF) for feature selection and (ANN-MLP) as a classifier (RCF-ANN). This study provides an experimental result that compares the performance before and after dimensional reduction.
Co-Authors Adnan Hassan Azanizawati Ma’aram Ibrahim Masood Mohd Syahril Ramadhan Mohd Saufi Waseem Alwan