Graph embedding-based Anomaly localization for HVAC system

Gu, Yuxuan; Li, Gen; Gu, Jiakai; Jung, Jason J.

doi:10.1016/j.jobe.2023.107511

Cited by 1 publication

(1 citation statement)

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“…In order to overcome this difficulty, a graph representation learning technique has been developed that embeds the dynamic graph. Yuxuan Gu et al used graph embedding techniques for anomaly detection in HVAC systems [40].…”

Section: Graph Embeddingmentioning

confidence: 99%

MGAD: Mutual Information and Graph Embedding Based Anomaly Detection in Multivariate Time Series

Huang,

Liu,

et al. 2024

Electronics

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Along with the popularity of mobile Internet and smart applications, more and more high-dimensional sensor data have appeared, and these high-dimensional sensor data have hidden information about system performance degradation, system failure, etc., and how to mine them to obtain such information is a very difficult problem. This challenge can be solved by anomaly detection techniques, which is an important field of research in data mining, especially in the domains of network security, credit card fraud detection, industrial fault identification, etc. However, there are many difficulties in anomaly detection in multivariate time-series data, including poor accuracy, fast data generation, lack of labeled data, and how to capture information between sensors. To address these issues, we present a mutual information and graph embedding based anomaly detection algorithm in multivariate time series, called MGAD (mutual information and graph embedding based anomaly detection). The MGAD algorithm consists of four steps: (1) Embedding of sensor data, where heterogeneous sensor data become different vectors in the same vector space; (2) Constructing a relationship graph between sensors using their mutual information about each other; (3) Learning the relationship graph between sensors using a graph attention mechanism, to predict the sensor data at the next moment; (4) Compare the predicted values with the real sensor data to detect potential outliers. Our contributions are as follows: (1) we propose an unsupervised outlier detection called MGAD with a high interpretability and accuracy; (2) massive experiments on benchmark datasets have demonstrated the superior performance of the MGAD algorithm, compared with state-of-the-art baselines in terms of ROC, F1, and AP.

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Section: Graph Embeddingmentioning

confidence: 99%