Abstract
This work describes the architecture for developing physics of failure models, derived as a function of machine sensor data, and integrating with data pertaining to other relevant factors like geography, manufacturing, environment, customer and inspection information, that are not easily modeled using physics principles. The mechanics of the system is characterized using surrogate models for stress and metal temperature based on results from multiple non-linear finite element simulations. A cumulative damage index measure has been formulated that quantifies the health of the component. To address deficiencies in the simulation results, a model tuning framework is designed to improve the accuracy of the model. Despite the model tuning, un-modelled sources of variation can lead to insufficient model accuracy. It is required to incorporate these un-modelled effects so as to improve the model performance. A novel machine learning based model fusion approach has been presented that can combine physics model predictions with other data sources that are difficult to incorporate in a physics framework. This approach has been applied to a gas turbine hot section turbine blade failure prediction example.
Highlights
The ability to accurately predict the failure of hot section components has been a critical problem to solve owing to the impending turbine down-time and high downstream damage costs in case of an un-intended failure
A novel machine learning based model fusion approach has been demonstrated that combines physics model predictions with other data sources that are difficult to incorporate in a physics framework
A detailed physics model is constructed for this work, which lays the foundation of the model
Summary
The ability to accurately predict the failure of hot section components (e.g. turbine blades, nozzles, rotor and combustor components) has been a critical problem to solve owing to the impending turbine down-time and high downstream damage costs in case of an un-intended failure. Accurate prediction of time to failure can enable interval extension of components that have sufficient life remaining, which provides more flexibility to operate the machine. The life of such components depends on many factors, such as operation conditions of the machine, material properties and associated variability, manufacturing variability and environment conditions to name a few. The physics based models mainly account for variability in operational behavior to capture the major failure modes. The other relevant features are chosen based on understanding of the system and are features that impact the failure of the component but are not accounted through physics models
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