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New framework boosts gas turbine emissions prediction with limited data

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a trust-aware probabilistic framework to improve emissions prediction for gas turbine fleets, particularly when labeled data is scarce. The system combines multiple machine learning models with confidence estimation and uncertainty quantification to generate reliability scores for predictions on unlabeled turbines. This approach significantly reduces prediction errors, with the highest-confidence predictions showing a substantial drop in Mean Absolute Error, indicating its potential for more trustworthy industrial deployments. AI

IMPACT Enhances the reliability of AI-driven predictive maintenance and monitoring in industrial settings.

RANK_REASON The cluster contains an academic paper detailing a new machine learning framework.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Rebecca Potts, Aiden Durrant, Rick Hackney, Georgios Leontidis ·

    Trust-Aware Predictive Emissions Monitoring for Gas Turbine Fleets with Limited Labelled Data

    arXiv:2606.06156v1 Announce Type: new Abstract: Machine learning-based predictive emissions monitoring systems offer a practical alternative to direct emissions measurement, but their deployment across gas turbine fleets is challenging when emissions labels are available for only…

  2. arXiv cs.LG TIER_1 English(EN) · Georgios Leontidis ·

    Trust-Aware Predictive Emissions Monitoring for Gas Turbine Fleets with Limited Labelled Data

    Machine learning-based predictive emissions monitoring systems offer a practical alternative to direct emissions measurement, but their deployment across gas turbine fleets is challenging when emissions labels are available for only a small subset of assets. In this work, a trust…

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