A clustering hyperrectangle model based on kernel density estimation for product quality detection

Abstract

With the rapid development of sensor technology, a huge amount of data is generated in the industrial manufacturing process, which poses new challenges for product quality detection. Meanwhile, the process data often have high‐dimensional characteristics of nonlinearity and strong correlation, so traditional methods are not suitable for quick quality detection. In this paper, to further enhance the speed and accuracy of quality detection, a clustering hyperrectangle model based on a kernel density estimation is proposed. First, based on the kernel density estimation, the distribution characteristics of data, which can directly provide optimal clustering parameter for the k‐means method, can be obtained. Next, to form the detection boundary of every hyperrectangle, the mean vector formed by clustering is used as the basis for determining the center of the hyperrectangle. Then, the distance between every sample point and the center of hyperrectangle is calculated for building the final detection condition. Furthermore, the joint determination based on the detection condition is used for effectively guaranteeing the detection accuracy. Finally, based on the simulation and experimental results, compared with the traditional methods, the proposed method can determine the detection condition in a more refined way, and has the merits of shorter test time and higher accuracy.