A One-Class Classification-Based Control Chart Using the K-Means Data Description Algorithm

Abstract

This paper aims to enlarge the family of one-class classification-based control charts, referred to as OC-charts, and extend their applications. We propose a new OC-chart using the K-means data description (KMDD) algorithm, referred to as KM-chart. The proposed KM-chart gives the minimum closed spherical boundary around the in-control process data. It measures the distance between the center of KMDD-based sphere and the new incoming sample to be monitored. Any sample having a distance greater than the radius of KMDD-based sphere is considered as an out-of-control sample. Phase I and II analysis of KM-chart was evaluated through a real industrial application. In a comparative study based on the average run length (ARL) criterion, KM-chart was compared with the kernel-distance based control chart, referred to as K-chart, and the k-nearest neighbor data description-based control chart, referred to as KNN-chart. Results revealed that, in terms of ARL, KM-chart performed better than KNN-chart in detecting small shifts in mean vector. Furthermore, the paper provides the MATLAB code for KM-chart, developed by the authors.

Application of Machine Learning in Statistical Process Control Charts: A Survey and Perspective

“…The results of this study show that the K chart is sensitive to small shifts in the mean vector, whereas the KNNDD chart is sensitive to moderate shifts in the mean vector. In addition, Gani and Limam 46 introduced a new chart using the KMDD algorithm and reported that their chart has a better performance in detecting small shifts of mean vector based on ARL than the K chart and KNNDD chart. To improve the performance of Kcharts, Maboudou-Tchao et al 47 used a one-class SVM technique based on the SVDD method for monitoring the mean vector based on Mahalanobis kernel.…”

Least Squares Generative Adversarial Networks-Based Anomaly Detection

“…For example, a novel control chart for the OCC based on a k-nearest neighbor algorithm was introduced [6]. Another OCC-based control chart using the 𝐾𝐾-means data description (KMDD) algorithm, which is called the KM-chart, was proposed by Gani and Limam [23]. Moreover, the OC-SVM that adapts the SVM to the OCC was suggested [10].…”

One‐class classification‐based monitoring for the mean and variance of time series

“…This approach has been adopted by several authors and proven useful in real-world circumstances. [17][18][19][20] Finally, the OCC method over classical statistical methods is that no limit theorems are used to determine a control limit. This can reduce the effort required to comply with the data dependency.…”

“…Herein, the radius of the hypersphere is determined according to preassigned significance levels. This approach has been adopted by several authors and proven useful in real‐world circumstances 17–20 . Finally, the OCC method over classical statistical methods is that no limit theorems are used to determine a control limit.…”