A structure-aware routing based anomaly detection for industrial multi-sensor time series

Abstract

Anomaly detection in multi-sensor time series (MTS) is a critical technology for ensuring the stable operation of modern industrial systems. Current mainstream methods identify anomalies by learning the structural consistency of normal data. As a result, natural structural breaks, a typical random non-stationary phenomenon in multi-sensor systems, are frequently misclassified as anomalies by these methods. To address this issue, we propose a Structure-Aware Routing (SaR) based Mixture-of-Experts (MoE) framework (SMoE) for anomaly detection. SMoE eliminates interference from structural breaks by assigning sensor series to specialized experts through SaR. First, the proposed SaR consists of Spatial Routing and Temporal Routing, which capture structural breaks at two levels: global breaks between sensors and local window-level breaks within individual sensors. Second, the SMoE-based anomaly detection framework can be applied to various sensor time series backbone networks, including large-scale models, significantly enhancing anomaly detection accuracy in MTS. Extensive experiments conducted on eight datasets across five industrial domains demonstrate that SMoE achieves an F1 score improvement ranging from 1% to 9% across four distinct backbone networks for anomaly detection. SMoE achieves an F1 score improvement of up to 8.4% compared to ten advanced baselines.