A graph embedded in graph framework with dual-sequence input for efficient anomaly detection of complex equipment under insufficient samples
H Yan, F Li, J Chen, Z Liu, J Wang, Y Feng… - Reliability Engineering & …, 2023 - Elsevier
Real-time anomaly detection is essential for the safe launch of some sophisticated
equipment, such as liquid rocket engines (LRE), in order to head off disasters. However, the …
equipment, such as liquid rocket engines (LRE), in order to head off disasters. However, the …
Hybrid anomaly detection via multihead dynamic graph attention networks for multivariate time series
In the real world, a large number of multivariate time series data are generated by Internet of
Things systems, which are composed of many connected sensing devices. Therefore, it is …
Things systems, which are composed of many connected sensing devices. Therefore, it is …
EA-GAT: Event aware graph attention network on cyber-physical systems
MY Yağci, MA Aydin - Computers in Industry, 2024 - Elsevier
Anomaly detection with high accuracy, recall, and low error rate is critical for the safe and
uninterrupted operation of cyber-physical systems. However, detecting anomalies in …
uninterrupted operation of cyber-physical systems. However, detecting anomalies in …
Anomaly Detection with Graph Attention Network for Multimodal IoT Data Monitoring
The popularity of the wireless network and the embedded system has promoted the
widespread application of the Internet of Things (IoT) monitoring system which detects …
widespread application of the Internet of Things (IoT) monitoring system which detects …
Full graph autoencoder for one-class group anomaly detection of IIoT system
Y Feng, J Chen, Z Liu, H Lv… - IEEE Internet of Things …, 2022 - ieeexplore.ieee.org
With the increasing automation and integration of equipment, it is urgent to carry out
anomaly detection (AD) for the large-scale system to ensure security, in virtue of Industrial …
anomaly detection (AD) for the large-scale system to ensure security, in virtue of Industrial …
Learning graph structures with transformer for multivariate time-series anomaly detection in IoT
Many real-world Internet of Things (IoT) systems, which include a variety of Internet-
connected sensory devices, produce substantial amounts of multivariate time-series data …
connected sensory devices, produce substantial amounts of multivariate time-series data …
From anomaly detection to classification with graph attention and transformer for multivariate time series
C Wang, G Liu - Advanced Engineering Informatics, 2024 - Elsevier
Numerous industrial environments and IoT systems in the real world contain a range of
sensor devices. These devices, when in operation, produce a large amount of multivariate …
sensor devices. These devices, when in operation, produce a large amount of multivariate …
GNN-Based Energy-Efficient Anomaly Detection for IoT Multivariate Time-Series Data
Anomaly detection is an important topic in the Internet of Things (IoT). Recently, some
anomaly detection methods based on graph neural networks (GNNs) have gained much …
anomaly detection methods based on graph neural networks (GNNs) have gained much …
Anomaly detection using spatial and temporal information in multivariate time series
Z Tian, M Zhuo, L Liu, J Chen, S Zhou - Scientific Reports, 2023 - nature.com
Real-world industrial systems contain a large number of interconnected sensors that
generate a significant amount of time series data during system operation. Performing …
generate a significant amount of time series data during system operation. Performing …
Edge Conditional Node Update Graph Neural Network for Multi-variate Time Series Anomaly Detection
H Jo, SW Lee - Information Sciences, 2024 - Elsevier
With the rapid advancement in cyber-physical systems, the increasing number of sensors
has significantly complicated manual monitoring of system states. Consequently, graph …
has significantly complicated manual monitoring of system states. Consequently, graph …