Anomaly Detection in Time Series Data Based on a Variable-Time Transformer

With the performance degradation caused by aging industrial equipment, multivariate time series anomaly detection has become pivotal for enabling Prognostics and Health Management PHM and preventive maintenance. However, existing methods face critical challenges in complex industrial scenarios, including insufficient real-time responsiveness, high noise sensi-tivity, and limited diversity in anomaly pattern recognition. To address these issues, this paper proposes VT-GAN, an anomaly detection framework that deeply integrates a Variable-Time Transformer VTT with Generative Ad-versarial Networks GANs . The model employs parallel generator groups, where each generator extracts multi-scale temporal patterns through dilated causal convolution. The VTT architecture combines temporal self-attention, variable-specific self-attention, and cross-attention layers, explicitly model-ing spatiotemporal interactions via learnable gating weights to effectively capture variable coupling effects under complex operating conditions. Fur-thermore, the integration of the Model-Agnostic Meta-Learning MAML framework enhances rapid adaptation to new tasks or environ-ments.Extensive experiments on six industrial datasets, including Secure Wa-ter Treatment SWaT and Server Machine Dataset SMD , demonstrate that VT-GAN outperforms the Transformer-GAN baseline with a 12.7 im-provement in F1-score average 89.3 , 23.4 reduction in false alarm rate, and real-time inference latency under 28 ms. Ablation studies validate the critical contributions of the multi-generator architecture F1-score improves by 3 and dynamic hybrid attention mechanism F1-score improves by 5.2 . This work provides a robust and reliable real-time monitoring solution for industrial equipment health management, demonstrating significant po-tential for industrial deployment.