Causality-informed Anomaly Detection in Partially Observable Sensor Networks: Moving beyond Correlations

TL;DR

This paper presents a causality-informed deep reinforcement learning approach for optimal sensor placement in partially observable systems, improving anomaly detection speed without relying on artificial interventions.

Contribution

It introduces a novel causal deep Q-network method that incorporates causal information into sensor placement, enhancing convergence and detection efficiency in real-world scenarios.

Findings

Faster convergence of the causal DQ approach.

Reduced anomaly detection time across various settings.

Effective in large-scale, real-world data streams.

Abstract

Nowadays, as AI-driven manufacturing becomes increasingly popular, the volume of data streams requiring real-time monitoring continues to grow. However, due to limited resources, it is impractical to place sensors at every location to detect unexpected shifts. Therefore, it is necessary to develop an optimal sensor placement strategy that enables partial observability of the system while detecting anomalies as quickly as possible. Numerous approaches have been proposed to address this challenge; however, most existing methods consider only variable correlations and neglect a crucial factor: Causality. Moreover, although a few techniques incorporate causal analysis, they rely on interventions-artificially creating anomalies-to identify causal effects, which is impractical and might lead to catastrophic losses. In this paper, we introduce a causality-informed deep Q-network (Causal DQ) approach for partially observable sensor placement in anomaly detection. By integrating causal information at each stage of Q-network training, our method achieves faster convergence and tighter theoretical error bounds. Furthermore, the trained causal-informed Q-network significantly reduces the detection time for anomalies under various settings, demonstrating its effectiveness for sensor placement in large-scale, real-world data streams. Beyond the current implementation, our technique's fundamental insights can be applied to various reinforcement learning problems, opening up new possibilities for real-world causality-informed machine learning methods in engineering applications.