Gas Turbine Component Qualification via Variance Modeling

The patent segments the qualification process into two distinct stages: (1) individual parameter measurement and initial assessment, and (2) holistic configuration evaluation using predictive models. This segmentation allows parameters to be checked individually for basic compliance while enabling the overall component configuration to be assessed separately, preventing automatic disqualification based solely on individual parameter deviations.

The patent transforms the qualification approach by changing from static individual parameter threshold checking to dynamic holistic configuration assessment. Predictive models (such as Gaussian process regression) are trained on historical data to learn the relationship between parameter variations and component performance, enabling the system to accommodate parameter variations that do not adversely affect overall component functionality.

The patent converts what would traditionally be considered harmful (parameter deviations from nominal values) into beneficial information for holistic assessment. Instead of treating parameter variations as defects to be eliminated, the system uses predictive models to evaluate whether these variations actually impact component performance, thereby converting potential waste into usable components.

The patent performs preliminary training of predictive models using historical manufacturing and performance data before actual qualification decisions are made. This preliminary action establishes the relationship between parameter variations and component performance in advance, enabling informed qualification decisions that prevent unnecessary scrapping while maintaining quality standards.

The patent creates a virtual digital twin or copy of the physical component through predictive modeling. Instead of physically testing each component or using complex physical measurement systems, the system creates a computational model that replicates component behavior based on parameter measurements, simplifying the assessment process while maintaining accuracy.

The patent replaces complex physical testing and evaluation mechanisms with computational predictive models. Instead of using elaborate physical test rigs or extensive manual inspection procedures, the system uses software-based Gaussian process regression and other statistical models to predict component performance, reducing physical complexity while improving efficiency.