Probabilistic Fatigue Life Prediction Using NDE Uncertainty Quantification
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Solution Overview
Problem
Current nondestructive examination (NDE) techniques face challenges in accurately predicting fatigue life due to uncertainties in flaw identification and sizing, particularly in field inspections of steel and alloy structures, where complex conditions and varying material properties complicate the reliability of ultrasonic inspection results.
Innovation Solution
A probabilistic method is developed for fatigue life prediction that incorporates uncertainty quantification models, using a probability of detection model and crack growth models to propagate uncertainties from NDE data and fatigue model parameters, allowing for the derivation of actual flaw size distributions and fatigue life predictions based on Bayes' theorem and Monte Carlo simulations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If deterministic treatment with safety factors is used, then the reliability of inspection results is improved, but the determination becomes complex and relies heavily on experience and expert judgment
Solution Approach 1:
The patent replaces the mechanical/expert judgment-based safety factor determination with a probabilistic mathematical model. By using probability density functions and statistical methods to quantify uncertainties in flaw detection and sizing, the system substitutes subjective expert assessment with objective computational analysis, thereby maintaining reliability while reducing complexity and experience dependency.
Solution Approach 2:
The patent transforms the deterministic safety factor parameter into probabilistic parameters including probability density functions of flaw sizes, detection probabilities, and uncertainty distributions. This parameter transformation allows for systematic quantification of uncertainties through mathematical modeling rather than relying on complex expert judgment processes.
2Measurement precision
If probabilistic modeling is used to quantify uncertainties, then the accuracy and informality of inspection results is improved, but the complexity of the analysis method increases
Solution Approach 1:
The patent segments the uncertainty analysis into distinct components: uncertainty in flaw detection probability, uncertainty in flaw sizing, uncertainty in crack growth model parameters, and uncertainty in material properties. By dividing the overall uncertainty into separable segments that can be modeled independently with probability density functions, the system achieves comprehensive accuracy while managing analytical complexity through modular decomposition.
Solution Approach 2:
The patent introduces probability density functions as intermediary mathematical tools that bridge the gap between uncertain input parameters and reliable output predictions. These PDFs serve as mediators that systematically propagate uncertainties through the crack growth model, transforming complex uncertainty relationships into manageable statistical computations that improve measurement precision without overwhelming complexity.
3Reliability
If field inspections are performed under complex service conditions, then the operational integrity assessment is improved, but the uncertainties in flaw identification and sizing increase significantly
Solution Approach 1:
The patent applies beforehand cushioning by incorporating uncertainty buffers into the probabilistic model before performing field inspections. By pre-defining probability density functions that account for expected variations in detection capability and sizing accuracy under different service conditions, the system compensates for anticipated measurement uncertainties, thereby maintaining reliable operational integrity assessment despite degraded measurement precision in field conditions.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach provides a systematic and rational method for managing uncertainties in NDE data, enhancing the reliability of fatigue life predictions and maintenance planning by quantifying the probability of detection and failure, thus improving the accuracy and informality of inspection results.
Implementation Method 1
state-of-the-art ultrasonic NDE techniques provide an opportunity to obtain the information about internal flaws of a structure, such as voids and cracks, without damaging the structure
Data Source
AI summary
A method for probabilistic fatigue life prediction using nondestructive testing data considering uncertainties from nondestructive examination (NDE) data and fatigue model parameters. The method utilizes uncertainty quantification models for detection, sizing, fatigue model parameters and inputs. A probability of detection model is developed based on a log-linear model coupling an actual flaw size with a nondestructive examination (NDE) reported size. A distribution of the actual flaw size is derived for both NDE data without flaw indications and NDE data with flaw indications by using probabilistic modeling and Bayes theorem. A turbine rotor example with real world NDE inspection data is presented to demonstrate the overall methodology.