Detection of Anomaly Points in High Dimensional Nonlinear Sensor Data Flow Based on SVDD Algorithm
GAO Feng
Abstract：Sensor data typically has high-dimensional features and may exhibit complex nonlinear relationships in actual industrial environments.Handling high-dimensional,nonlinear,and complex data features is a challenging issue in current research.This study is based on the Support Vector Data Description(SVDD)algorithm for anomaly detection in high-dimensional nonlinear sensor data streams.Generative adversarial networks are used to extract high-dimensional nonlinear data features,and principal component analysis is employed to reduce the dimensionality of the extracted features.The SVDD model is trained using dimensionality-reduced data,the dual problem is solved,and parameters such as support vector coefficients and thresholds are obtained to determine the decision boundary for anomaly detection.Anomaly point detection is achieved Based on the decision boundary.Experimental verification show that the proposed method has a high anomaly detection rate and a low false alarm rate.The study concludes that the SVDD algorithm is effective in detecting outliers in high-dimensional nonlinear sensor data streams,showcasing its potential value in practical engineering applications.
Keywords：SVDD algorithmsensor data flowoutliersgenerating adversarial networksprincipal component analysis
Publication Date：2024-03-20
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：5( 65-69 )