用 LLM 结合文本与传感器数据过滤工业训练数据，虚拟流量计 MAPE 从 7.1% 降至 5.4%
Text-Informed Training Data Filtering for Enhanced Virtual Flow Meter Performance
这篇论文教你用 LLM 读工程师备注来清洗工业传感器数据，虚拟流量计的 MAPE 直接从 7.1% 降到 5.4%，做工业机器学习的可以看看思路。
一篇论文提出用 LLM 做文本信息驱动的训练数据过滤方法，把工程师备注、操作日志等非结构化文本与表格化传感器数据一起做上下文异常检测，再生成二值掩码过滤训练集。下游评估目标是稳态软传感器回归模型——用于估算油井流量的虚拟流量计。在合成油气数据集上的实验显示，多模态过滤将下游 MAPE 从 7.1% 降到 5.4%，优于传统基于规则的过滤。消融实验证明操作员文本评论提供了仅靠传感器数据得不到的过滤信号，可替代人工数据清洗。
Text-Informed Training Data Filtering for Enhanced Virtual Flow Meter Performance
In sectors such as energy, aquaculture and chemical processing, the effectiveness of both mechanistic and data-driven models is frequently limited by poor-quality training data, which obscures underlying physical laws and degrades predictive accuracy. Such quality issues commonly manifest as anomalies in the dataset, ranging from sensor faults to process upsets and incorrect operator inputs. While traditional rule-based filtering can remove a subset of the anomalies, it is unable to leverage contextual information stored as unstructured text, such as engineer comments and operational logs that explain under what conditions the data was collected. We propose a text-informed data filtering method that leverages large language models (LLMs) to integrate unstructured operational data with tabular sensor data. The method consists of two steps: (1) contextual anomaly detection by reasoning over both data modalities, and (2) generation of a binary mask that is applied to filter the training dataset for model development. To investigate the potential benefit, a steady-state soft-sensor regression model, namely a virtual flow meter that estimates well flow rates, is used as the downstream evaluation target. Experiments on a synthetic oil and gas dataset show improved model performance compared to traditional rule-based filtering. Ablation studies show that textual operator comments can provide filtering-relevant signals beyond sensor data alone, with the multimodal approach reducing downstream MAPE from 7.1% to 5.4% relative to sensor-only filtering. This work demonstrates that integrating unstructured operational knowledge with sensor data through LLMs can improve data quality for industrial machine learning applications, offering a scalable alternative to manual data curation.
