Component Life Estimation Using Probabilistic Crack Growth Models
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Solution Overview
Problem
Current methods for estimating the life of electromechanical machine components, such as gas turbines, are overly conservative due to uncertainties in material properties and initial flaw sizes, leading to premature service intervals and increased costs.
Innovation Solution
A system and method that optimize crack growth rate models and temperature models using a joint optimization method, incorporating a scatter parameter to estimate component life, which accounts for non-Gaussian distributions in material data, enhancing the accuracy of life-value predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If deterministic approach with extreme values is used for life estimation, then reliability is improved, but productivity deteriorates due to premature service intervals
Solution Approach 1:
The patent transforms the deterministic extreme-value approach into a probabilistic approach by changing the parameters from fixed extreme values to distributed statistical parameters. This allows life estimation to account for material property variations and initial flaw distributions, resulting in more accurate predictions that extend service intervals while maintaining reliability.
Solution Approach 2:
The patent replaces the conservative deterministic mechanical estimation method with a probabilistic statistical methodology. By substituting the deterministic framework with probabilistic fracture mechanics, the system achieves more realistic life predictions that reduce premature servicing while ensuring component reliability.
2Ease of operation
If conventional least-squares regression is used for modeling, then ease of operation is improved, but measurement precision deteriorates for non-Gaussian distributed data
Solution Approach 1:
The patent changes the modeling approach from least-squares regression to maximum likelihood estimation (MLE). This parameter change in the statistical methodology allows accurate modeling of non-Gaussian distributed material data, improving the precision of scatter parameter estimates while maintaining computational feasibility through iterative optimization algorithms.
Solution Approach 2:
The patent substitutes the conventional least-squares regression methodology with maximum likelihood estimation. This substitution replaces a method optimized for Gaussian distributions with one that can handle arbitrary distributions, thereby improving measurement precision for material property scatter while preserving ease of operation through standardized statistical software implementations.
3Reliability
If conservative life estimation is used, then reliability is improved, but loss of time increases due to premature component replacement
Solution Approach 1:
The patent changes the estimation parameters from conservative deterministic values to probabilistic distributions that reflect actual material behavior. This allows calculation of reliable life predictions with quantified uncertainty, enabling extended component service life while maintaining reliability through statistically-based decision-making rather than premature replacement.
Data Source
AI summary
A method for estimating life of a component includes obtaining fracture data corresponding to a component. The fracture data includes a first dataset corresponding to a threshold region where the crack in the component is dormant below a fatigue threshold. The method further includes determining initial estimates of parameters of a crack growth rate model and parameters of temperature models corresponding to the crack growth rate model based on the fracture data. The method also includes computing optimized parameters of temperature models corresponding to the crack growth rate model, and a scatter parameter via simulation of a joint optimization method using the initial estimates. The method includes determining a cumulative distribution function based on the optimized parameters and the scatter parameter and estimating life of the component based on the cumulative distribution function.