Physics-informed gated recurrent graph attention unit network for anomaly detection in industrial cyber-physical systems
Industrial cyber-physical systems (ICPSs) play an important role in many critical
infrastructures. To ensure the secure and reliable operation of ICPSs, this work presents a …
infrastructures. To ensure the secure and reliable operation of ICPSs, this work presents a …
Autoregressive data generation method based on wavelet packet transform and cascaded stochastic quantization for bearing fault diagnosis under unbalanced …
The capacity to diagnose faults in rolling bearings is of significant practical importance to
ensure the normal operation of the equipment. However, because it is challenging to obtain …
ensure the normal operation of the equipment. However, because it is challenging to obtain …
Few-shot time-series anomaly detection with unsupervised domain adaptation
Anomaly detection for time-series data is crucial in the management of systems for
streaming applications, computational services, and cloud platforms. The majority of current …
streaming applications, computational services, and cloud platforms. The majority of current …
Ensembled masked graph autoencoders for link anomaly detection in a road network considering spatiotemporal features
Road anomaly detection aims to find a small group of roads that are exceptional with respect
to the rest of the physical links in a transportation network, posing great challenges for …
to the rest of the physical links in a transportation network, posing great challenges for …
Anomaly detection in industrial machinery using IoT devices and machine learning: A systematic map**
Anomaly detection is critical in the smart industry for preventing equipment failure, reducing
downtime, and improving safety. Internet of Things (IoT) has enabled the collection of large …
downtime, and improving safety. Internet of Things (IoT) has enabled the collection of large …
An adversarial contrastive autoencoder for robust multivariate time series anomaly detection
J Yu, X Gao, F Zhai, B Li, B Xue, S Fu, L Chen… - Expert Systems with …, 2024 - Elsevier
Multivariate time series (MTS), whose patterns change dynamically, often have complex
temporal and dimensional dependence. Most existing reconstruction-based MTS anomaly …
temporal and dimensional dependence. Most existing reconstruction-based MTS anomaly …
Multiview graph contrastive learning for multivariate time-series anomaly detection in IoT
Internet of Things (IoT) systems typically generate large amounts of sensory signals that get
involved to represent the states of the systems. Most existing methods focus on learning the …
involved to represent the states of the systems. Most existing methods focus on learning the …
A survey of time series anomaly detection methods in the aiops domain
Internet-based services have seen remarkable success, generating vast amounts of
monitored key performance indicators (KPIs) as univariate or multivariate time series …
monitored key performance indicators (KPIs) as univariate or multivariate time series …
A filter-augmented auto-encoder with learnable normalization for robust multivariate time series anomaly detection
J Yu, X Gao, B Li, F Zhai, J Lu, B Xue, S Fu, C **ao - Neural Networks, 2024 - Elsevier
While existing reconstruction-based multivariate time series (MTS) anomaly detection
methods demonstrate advanced performance on many challenging real-world datasets, they …
methods demonstrate advanced performance on many challenging real-world datasets, they …
Multivariate time series anomaly detection via separation, decomposition, and dual transformer-based autoencoder
S Fu, X Gao, B Li, F Zhai, J Lu, B Xue, J Yu… - Applied Soft Computing, 2024 - Elsevier
Multivariate time series usually have entangled temporal patterns and various anomaly
types. Meanwhile, they often contain both continuous and discrete features. Many existing …
types. Meanwhile, they often contain both continuous and discrete features. Many existing …