5 papers
TPA-AD: A Two-Stage Pseudo Anomaly-Guided Method for Bearing Time-Series Anomaly Detection
Xiancheng Wang, Zhibo Zhang, Ran Li +4
This paper proposes a two-stage pseudo anomaly-guided anomaly detection method (\textbf{T}wo-stage \textbf{P}seudo \textbf{A}nomaly-guided \textbf{A}nomaly \textbf{D}etection, \tex…
Fourier-KAN-Mamba: A Novel State-Space Equation Approach for Time-Series Anomaly Detection
Xiancheng Wang, Lin Wang, Rui Wang +2
Time-series anomaly detection plays a critical role in numerous real-world applications, including industrial monitoring and fault diagnosis. Recently, Mamba-based state-space mode…
PD-SOVNet: A Physics-Driven Second-Order Vibration Operator Network for Estimating Wheel Polygonal Roughness from Axle-Box Vibrations
Xiancheng Wang, Lin Wang, Rui Wang +5
Quantitative estimation of wheel polygonal roughness from axle-box vibration signals is a challenging yet practically relevant problem for rail-vehicle condition monitoring. Existi…
BoundAD: Boundary-Aware Negative Generation for Time Series Anomaly Detection
Xiancheng Wang, Lin Wang, Zhibo Zhang +2
Contrastive learning methods for time series anomaly detection (TSAD) heavily depend on the quality of negative sample construction. However, existing strategies based on random pe…
Fast-Slow Co-advancing Optimizer: Toward Harmonious Adversarial Training of GAN
Lin Wang, Xiancheng Wang, Rui Wang +2
Up to now, the training processes of typical Generative Adversarial Networks (GANs) are still particularly sensitive to data properties and hyperparameters, which may lead to sever…