东北大学主页平台姚羽--中文主页-- A generalizable anomaly detection framework with dynamic concept drift suppression for non-stationary time series

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发布时间：2026-02-12 点击次数：

发表刊物：Knowledge-Based Systems
摘要：In practical applications, the performance of industrial data stream anomaly detection methods often degrades due to concept drift. The core bottleneck lies in the fact that existing algorithms struggle to dynamically perceive the coupling relationship between data distribution changes and anomaly patterns. This paper proposes a generalized framework for time series anomaly detection based on Dynamic Drift Awareness and Diffusion Enhancement (DDADE). Through real-time distance monitoring and an adaptive model incremental learning mechanism, it achieves collaborative detection of concept drift and anomaly events. Specifically, the innovation of this work is as follows: First, a drift detection module based on the industrial-enhanced Mahalanobis distance is designed to capture the covariate shift in the feature space in real-time. Second, an anomaly detection model based on diffusion enhancement is proposed, which can perform incremental learning or dynamically adjust the threshold according to the drift detection results. Experiments show that in several representative industrial simulation datasets containing drift scenarios, this method outperforms the baseline models.
关键字：Threshold adjustment, Concept drift, Incremental update, Anomaly detection
论文类型：SCI JCR Q1
备注：https://www.sciencedirect.com/science/article/pii/S0950705126001231
学科门类：工学
文献类型：JCR 一区
是否译文：否