Journal:Knowledge-Based Systems

Abstract: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.

Key Words:Threshold adjustment, Concept drift, Incremental update, Anomaly detection

Indexed by:SCI JCR Q1

Note:https://www.sciencedirect.com/science/article/pii/S0950705126001231

Discipline:Engineering

Document Type:JCR 一区

Translation or Not:no