An Accurate and Interpretable Framework for Trustworthy Process Monitoring

1. 1Identify physically meaningful semantics in process monitoring data
2. 2Develop self-attentive models that incorporate these semantics
3. 3Evaluate model performance using metrics such as accuracy and interpretability
4. 4Implement the framework in energy conversion plants to ensure safety and reliability

Data scientists and AI engineers on a team can benefit from this framework to ensure safety in energy conversion plants, and it can be applied by process control engineers and plant operators to improve monitoring and decision-making

💡 Incorporating physically meaningful semantics into self-attentive models can improve trustworthiness in process monitoring

💡 Trusted process monitoring in energy conversion plants with accurate & interpretable models