Proposed Quality Control Charts Using Haar Wavelet Coefficients for Enhanced Production Monitoring

One main problem of the traditional quality control charts, such as the Individual Observations Chart and the Moving Average Chart, is that they do not focus on monitoring the differences in the produced materials. To address this issue, researchers suggested creating new charts based on the Haar wavelet that could potentially put more focus and better handle the data noise affecting traditional charts' accuracy. The new proposed charts are based on a method called wavelet transform for Haar wavelet. One chart records the average of individual observations (Approximate coefficients or low pass filter) while the other monitors the variations among these observations (Detail coefficients or high pass filter). For the first time, the universal threshold method to treat data noise was used to create control limits in the proposed charts. The researchers used both simulated and real data to develop these charts using MATLAB software. The study proved the accuracy and efficiency of the proposed charts, their success in handling the data noise, and their sensitivity in detecting minor changes that may occur in the production process.