Tabular Machine Learning Methods for Predicting Gas Turbine Emissions

A Physics - Based Emissions Model for Aircraft Gas Turbine

In this thesis, a physics-based model of an aircraft gas turbine combustor is developed for predicting NO, and CO emissions. The objective of the model is to predict the emissions of current and potential future gas turbine engines within quantified uncertainty bounds for the purpose of assessing design tradeoffs and interdependencies in a policy-making setting. The approach taken is to capture...

An evaluation of machine-learning methods for predicting pneumonia mortality

This paper describes the application of eight statistical and machine-learning methods to derive computer models for predicting mortality of hospital patients with pneumonia from their findings at initial presentation. The eight models were each constructed based on 9847 patient cases and they were each evaluated on 4352 additional cases. The primary evaluation metric was the error in predicted...

Machine Learning Methods for Predicting HLA–Peptide Binding Activity

As major histocompatibility complexes in humans, the human leukocyte antigens (HLAs) have important functions to present antigen peptides onto T-cell receptors for immunological recognition and responses. Interpreting and predicting HLA-peptide binding are important to study T-cell epitopes, immune reactions, and the mechanisms of adverse drug reactions. We review different types of machine lea...

Application of Statistical Methods for Gas Turbine Plant Operation Monitoring

Vibration Monitoring of Gas Turbine Engines: Machine-Learning Approaches and Their Challenges

In this study, condition monitoring strategies are examined for gas turbine engines using vibration data. The focus is on data-driven approaches, for this reason a novelty detection framework is considered for the development of reliable data-driven models that can describe the underlying relationships of the processes taking place during an engine’s operation. From a data analysis perspective,...