Industrial sensor networks produce complex signals with nonlinear structure and shifting distributions. We propose RIE-SenseNet, a novel geometry-aware Transformer model that embeds sensor data in a Riemannian manifold to tackle these challenges. By leveraging hyperbolic geometry for sequence modeling and introducing a manifold-based augmentation technique, RIE-SenseNet preserves sensor signal structure and generates realistic synthetic samples. Experiments show RIE-SenseNet achieves >90% F1-score, far surpassing CNN and Transformer baselines. These results illustrate the benefit of combining non-Euclidean feature representations with geometry-consistent data augmentation for robust pattern recognition in industrial sensing.

翻译：工业传感器网络产生的信号具有非线性结构和时变分布特性。为此，我们提出RIE-SenseNet——一种新颖的几何感知Transformer模型，通过将传感器数据嵌入黎曼流形来应对这些挑战。该方法利用双曲几何进行序列建模，并引入基于流形的数据增强技术，从而保持传感器信号的结构特征并生成逼真的合成样本。实验表明，RIE-SenseNet的F1分数超过90%，显著优于CNN和Transformer基线模型。这些结果证明了将非欧几里得特征表示与几何一致的数据增强相结合，对于工业传感中的鲁棒模式识别具有重要价值。