Method for simulation assisted data generation and deep learning intelligence creation in non-destructive evaluation systems

1 . A method for detecting one or more anomalies in an object, the method comprising:

receiving experimental data of the object, wherein the experimental data is obtained while testing one or more anomalies in the object, and wherein the experimental data is associated with predefined anomaly types of the object;

applying a probability density function (PDF), representative of variability in the experimental data arising from measurement conditions, upon one or more variables associated with the experimental data to determine corresponding one or more PDF estimates, wherein the one or more variables correspond to measurement-system parameters affecting acquisition of the experimental data and comprise at least one of instrument noise, transducer sensitivity variation, and expected defect morphology parameters, wherein the PDF application identifies variations and sources of variations in the experimental data attributable to the one or more variables;

generating stochastic synthetic data corresponding to each of the one or more variables based on the corresponding one or more PDF estimates;

providing the stochastic synthetic data to a simulator model to generate simulated data associated with the object based on the stochastic synthetic data and priori data associated with testing of the object, wherein the simulated data is generated based on characteristics of the object indicated by the priori data, and wherein the simulated data comprises one or more statistically synthesized anomalies not present in the experimental data along with the one or more anomalies of the experimental data;

training a learning model based on the one or more statistically synthesized anomalies and the one or more anomalies of the experimental data;

validating the trained learning model using a portion of the experimental data of the object, and

wherein the trained learning model is applied to detect anomalies in a new object related to the object,

wherein the object is one of a part, a product, a weld, a system, an instrument, and a component,

wherein the one or more anomalies include one of a size, shape, and orientation object,

wherein the learning model is retrained based on a result of the validation, and

wherein the one or more variables are defined based on registered experimental data such that defect statistics are independent of positional variation in the experimental data.

2 . The method as claimed in claim 1 , wherein the one or more variables further comprises at least one of experimental variation, defect probabilities, and precision and accuracy of the measurement.

3 . The method as claimed in claim 1 , wherein the experimental data comprises 1D data, 2D data, 3D volumetric data and 4D time-stamped data.

4 . The method as claimed in claim 1 , wherein the priori data comprises one or more characteristics of the object.

5 . A system for detecting one or more anomalies in an object, the system comprising:

at least one processor; and

a memory storing instructions that, when executed by the at least one processor, cause the system to:

receive experimental data of the object, wherein the experimental data is obtained while testing one or more anomalies in the object, and wherein the experimental data is associated with predefined anomaly types of the object;

apply a probability density function (PDF), representative of variability in the experimental data arising from measurement conditions, upon one or more variables associated with the experimental data to determine corresponding one or more PDF estimates,

wherein the one or more variables correspond to measurement-system parameters affecting acquisition of the experimental data and comprise at least one of instrument noise, transducer sensitivity variation, and expected defect morphology parameters, wherein the PDF application identifies variations and sources of variations in the experimental data attributable to the one or more variables,

generate stochastic synthetic data corresponding to each of the one or more variables based on the corresponding one or more PDF estimates;

provide the stochastic synthetic data to a simulator model to generate simulated data associated with the object based on the stochastic synthetic data and priori data associated with the testing of the object, wherein the simulated data is generated based on characteristics of the object indicated by the priori data, and wherein the simulated data comprises one or more statistically synthesized anomalies not present in the experimental data along with the one or more anomalies of the experimental data,

train a learning model based on the one or more statistically synthesized anomalies and the one or more anomalies of the experimental data,

validate the trained learning model using a portion of the experimental data of the object, and

apply the trained learning model to detect anomalies in a new object related to the object,

wherein the object is one of a part, a product, a weld, a system, an instrument, and a component,

wherein the one or more anomalies include one of a size, shape, and orientation,

wherein the learning model is retrained based on a result of the validation, and

wherein the one or more variables are defined based on registered experimental data such that defect statistics are independent of positional variation in the experimental data.

6 . The system as claimed in claim 5 , wherein the one or more variables further comprises at least one of experimental variation, defect probabilities, and precision and accuracy of the measurement.

7 . The system as claimed in claim 5 , wherein the experimental data comprises 1D data, 2D data, 3D volumetric data and 4D time-stamped data.

8 . The system as claimed in claim 5 , wherein the priori data comprises one or more characteristics of the object.

9 . The method as claimed in claim 1 , wherein the one or more variables comprises at least one of instrument noise, transducer sensitivity variations, expected defect morphologies, and precision and accuracy of the measurement.

10 . The method as claimed in claim 1 , further comprising:

integrating the learning model with a non-destructive evaluation/testing (NDE/NDT) hardware.

11 . The method as claimed in claim 10 , wherein the learning model is applied with the NDE/NDT hardware for implementing an NDE/NDT process.

12 . The method as claimed in claim 11 , wherein the NDE/NDT process is one or more of a radiography testing, an ultrasonic phased array imaging, a liquid penetrant testing, a magnetic particle testing, and an active infrared imaging.

13 . The method as claimed in claim 11 , wherein the NDE/NDT process is implemented using an NDE/NDT transducer that is one or more of an ultrasonic transducer and a phased array probe.

14 . The method as claimed in claim 1 , wherein the priori data is a computer-aided design (CAD) model.

15 . The system as claimed in claim 5 , wherein the one or more variables comprises at least one of instrument noise, transducer sensitivity variations, expected defect morphologies, and precision and accuracy of the measurement.

16 . The system as claimed in claim 5 , wherein the system is connected to a non-destructive evaluation/testing (NDE/NDT) hardware, and

wherein the system is configured to integrate the learning model with the NDE/NDT hardware.

17 . The system as claimed in claim 16 , wherein the learning model is applied with the NDE/NDT hardware for implementing an NDE/NDT process.

18 . The system as claimed in claim 17 , wherein the NDE/NDT process is one or more of a radiography testing, an ultrasonic phased array imaging, a liquid penetrant testing, a magnetic particle testing, and an active infrared imaging.

19 . The system as claimed in claim 17 , wherein the system and the NDE/NTE hardware are both connected to a NDE/NDT transducer, and wherein the NDE/NDT process is implemented using the NDE/NDT transducer that is one or more of an ultrasonic transducer and a phased array probe.

20 . The system as claimed in claim 5 , wherein the priori data is a computer-aided design (CAD) model.