Papers for
industrial machine learning teams
Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.
Engineering models face challenges quantifying uncertainty under changing conditions
Benchmarking uncertainty quantification for engineering regression under graded distribution shift
Abstract: Machine learning models in engineering are often used beyond their training conditions, where reliable uncertainty quantification (UQ) matters most. Existing comparisons of regression UQ methods rely on random splits, which assess calibration only in distribution. We introduce a three-rung shift ladder (in distribution, soft shift and hard shift) built along physically meaningful shift variables, and evaluate 12 methods from five families on seven public engineering datasets: five extrapolation settings, an unseen turbofan fault mode and a winter air-quality sensor deployment. All tuning uses source data only. In distribution, conformal methods cover at least 0.946 of test points on every dataset. Under hard shift, mean coverage falls to 0.40-0.75 across the 11 methods that always return an interval, and no systematic difference in method ranks is detected (Friedman p = 0.63). Weighted conformalised quantile regression stays near nominal on the points it answers, but abstains on 85\% of hard-shift points on average and covers only 0.67 of the few Concrete points it answers. Source-validation gains predict improvement in distribution and under soft shift, but not under hard shift (Spearman's rho = 0.04). Engineering evaluations should split calibration data by deployment unit, report subgroup coverage and sharpness, and treat abstention as an explicit operational outcome.
Text helps filter training data to improve virtual flow meter accuracy
Text-Informed Training Data Filtering for Enhanced Virtual Flow Meter Performance
Abstract: 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.