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 a small subset of assets. In this work, a trust-aware probabilistic framework is proposed for fleet-level gas turbine NOx prediction under limited labelled supervision. The framework combines a multi-head recurrent prediction model with learned confidence estimation, ensemble-based uncertainty quantification, auxiliar
The increasing push for sustainability and stricter emissions regulations is driving demand for advanced monitoring solutions, while AI/ML advancements make such predictive systems more feasible.
This development allows for more accurate and cost-effective emissions monitoring for industrial fleets, critical for environmental compliance and operational efficiency.
Emissions monitoring can shift from direct measurement, which can be costly and data-intensive, to more widely deployable predictive models leveraging limited labelled datasets.
- · Industrial gas turbine operators
- · Environmental compliance technology providers
- · Machine learning solution developers
- · Power generation sector
- · Traditional direct emissions measurement companies
Improved environmental compliance and reduced operational costs for gas turbine fleets.
Accelerated adoption of AI-driven predictive maintenance and monitoring across other industrial sectors.
Potential for new regulatory frameworks that integrate AI-based predictive emissions monitoring as a standard.
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Read at arXiv cs.LG