提出新架构提升管道泄漏检测在工况变化下的鲁棒性
MGSB: Manifold Gated Signature Branch Pressure-Domain Baseline Architecture for Two-Phase Pipeline Flows Under Distributional Shift
- 通过分组特征融合与动态门控机制感知流态变化
- 跨域检测F1达0.783，显著优于传统模型
- 适合工业场景中传感器多样、工况多变的泄漏监测
多相管道泄漏检测模型在部署时若遭遇与训练阶段不同的流态，性能常会下降。现有评估通常仅在分布内条件下进行，掩盖了气泡流到塞流等工况转换带来的失效问题。本文提出曼达托门控签名偏置（MGSB）架构，结合工况条件特征融合、TT-粗路径编码器及均值教师一致性正则化，增强分布外鲁棒性。在留一组外推评估下，该模型实现0.930的分布内检测F1和0.783的分布外F1，显著优于CNN-LSTM与全连接基线，在严重特征扰动下表现突出。消融实验表明，架构设计是分布外鲁棒性的主因；马哈拉诺比斯距离分析确认留出条件确为分布外。结果表明，显式建模工况变化是实现工业级多相管道无感泄漏检测的可行路径。
原文摘要 · Abstract (English)
Leak detection models for multiphase pipelines often degrade when deployed under flow regimes that differ from training. Existing evaluations typically assess performance under in-distribution operating conditions, masking failures caused by regime transitions such as bubble-to-slug flow. We propose the Manifold Gated Signature Bias (MGSB), a regime-aware architecture combining regime-conditioned feature fusion, a TT-RoughPath encoder, and Mean-Teacher consistency regularization to improve robustness under distribution shift. Under leave-one-group-out evaluation, MGSB achieves a detection F1 of 0.930 and an OOD F1 of 0.783, substantially outperforming CNN-LSTM and fully connected baselines under severe feature corruption. Ablations show the proposed architecture, not the training procedure, is the primary contributor to OOD robustness, while Mahalanobis-distance analysis confirms the held-out conditions are genuinely out-of-distribution. These results show that explicit regime-aware modelling is a practical path toward robust, sensor-agnostic leak detection in industrial multiphase pipelines.
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