Data De-Noise for Multivariate T2 and S-Charts Using Multivariate Wavelets
DOI:
https://doi.org/10.21271/zjhs.28.6.19Keywords:
Multivariate charts, Multivariate wavelet, De-noise, Threshold, Quality control.Abstract
In this research, proposed multivariate charts were created corresponding to T2 and S-charts that are robust to noise data by using multivariate wavelet shrinkage, that dealt with the contamination problem before constructing Shewhart charts, through several different wavelets with (Baye), and (SURE) threshold methods, based on the rule of soft thresholding. It is then compared with the classical method proposed by Shewhart based on total variance (trace of the variance matrix), generalized variance (determinant of the variance matrix), and process capability. A MATLAB program designed to obtain the most efficient charts with the least contamination is used to simulate and use real data to get the most efficient charts with the least contamination. Based on the study's conclusions, the proposed charts are more efficient than the classical method in de-noising the data.
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Copyright (c) 2024 Dler Hussein Kadir, Rebaz Othman Yahya, Azhin Muhammed Khudhur, Taha Hussein Ali
This work is licensed under a Creative Commons Attribution 4.0 International License.
