A Hybrid Algorithm Incorporating Vector Quantization and One-Class Support Vector Machine for Industrial Anomaly Detection

作者：Jingxuan Pang, Xiaokun Pu, Chunguang Li · 发表于：IEEE Transactions on Industrial Informatics · 年份：2022 · DOI：10.1109/tii.2022.3145834 · 被引用次数：83 · 研究领域：Anomaly Detection Techniques and Applications、Network Security and Intrusion Detection、Fault Detection and Control Systems

Anomaly detection plays an important role in industry, especially in ensuring system safety and product quality. Due to the unavailability of anomalous data in many practical cases, anomaly detection is usually solved by one-class classification (OCC) methods using only normal data. As a classical OCC method, one-class support vector machine (OCSVM) is a popular discriminative approach for anomaly detection, which detects abnormal data points by establishing a decision boundary in the kernel space. However, the performance of OCSVM heavily relies on kernel parameters, whose selection is not trivial for anomaly detection problems. Moreover, for some uneven and complex data distributions, different data regions may have quite different densities and shapes, making it difficult for OCSVM to obtain good boundaries in all regions using a global kernel parameter. To address the above two issues, in this article, we propose a hybrid algorithm incorporating vector quantization and OCSVM (VQ-OCSVM). Specifically, vector quantization is used to extract distribution information of normal data, and the results are used to construct an explicit mapping function to map data into a high-dimensional feature space. Then, OCSVM is performed in the feature space to build a classifier. By introducing the explicit mapping into OCSVM, the proposed method can effectively bypass the kernel parameter selection problem of the classical OCSVM method. Furthermore, the constructed mapping carries the dat...