Predictive Maintenance System for Wafer Transfer Robot Using Gaussian Mixture Model and Mean-Shift Clustering

Abstract

Equipment maintenance is the technology of monitoring the condition of equipment and predicting the error of the equipment. As an interest in equipment maintenance increases, many studies are being conducted on methods for minimizing economic losses due to equipment failure or enormous maintenance costs, especially in the semiconductor manufacturing field. In this paper, a new predictive maintenance (PdM) method is proposed to predict the error of the wafer transfer robot and to classify the significance level of the error. The experimental data of the wafer transfer robot were collected using acceleration sensor. To analyse the data, pre-processing was performed using fast Fourier transformation (FFT). The error levels were defined by classifying the number of error areas into 1, 2 and 3 regions. The Gaussian mixture model (GMM) algorithm was used to classify normal and abnormal states and mean-shift (MS) algorithm was used to classify the error level for the data classified as abnormal state. The proposed method can detect errors and classify the error level accurately in the data where normal data and error data were mixed. The advantage of the proposed method is that it can accurately distinguish errors from normal data by applying GMM with higher accuracy that previous studies and classify the level of errors using MS.