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US 12,674,780 B2Granted
System and method for automated detection, classification, and remediation of defects using ultrasound testing
AssigneeSaudi Arabian Oil Company
InventorsBashaer Abdullah Alhammad, Hasan Ali Al-Hashmy, Abdulaziz Yahya Asiri, Khalid Salem Al-Ghamdi
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Abstract
A system and method perform automated detection, classification, and remediation of defects in a structure using ultrasound testing. The system includes an autoencoder is trained and configured to generate a de-noised UT scan image from a noisy UT scan image of a structure, a support vector machine configured to detect a defect in the structure, a convolutional neural network configured to classify the defect, and a remediation subsystem configured to remediate the defect. The method implements the system.
Claims20
A system, comprising:
- an input device configured to receive a noisy ultrasonic test (UT) scan image of a structure, wherein the noisy UT scan image includes noise and a plurality of features, and wherein the noisy UT scan image has a first size;
- and
- a processor configured by code executing therein to implement:
- a UT scan de-noiser configured to generate a de-noised UT scan image from the noisy UT scan image, the UT scan de-noiser including:
- an encoder having a first layer of artificial neurons to compress the noisy UT scan image to an encoded representation having a second size at least two orders of magnitude smaller than the first size;
- a bottleneck having a second layer of artificial neurons, responsive to the encoded representation, to pass only the plurality of features as an output;
- and
- a decoder having a third layer of artificial neurons to decompress the output of the bottleneck and to generate the de-noised UT scan image from the decompressed output;
- a defect detector configured to automatically determine, without user input, whether the structure has a defect or does not have a defect from the de-noised UT scan image;
- a defect classifier configured, responsive to the automatic classification of the structure as having a defect, to classify the defect by type;
- and
- a remediation subsystem configured to remediate the classified defect.
9 more elements
A system, comprising:
- an autoencoder configured to generate a de-noised UT scan image from a noisy UT scan image of a structure, wherein the noisy UT scan image includes noise and a plurality of features, and wherein the noisy UT scan image has a first size, the autoencoder including:
- an encoder having a first layer of artificial neurons to compress the noisy UT scan image to an encoded representation having a second size at least two orders of magnitude smaller than the first size;
- a bottleneck having a second layer of artificial neurons, responsive to the encoded representation, to pass only the plurality of features as an output;
- and
- a decoder having a third layer of artificial neurons to decompress the output of the bottleneck and to generate the de-noised UT scan image from the decompressed output;
- a support vector machine (SVM) configured to automatically determine, without user input, whether the structure has a defect or does not have a defect from the de-noised UT scan image;
- a convolutional neural network (CNN) configured, responsive to the automatic classification of the structure as having a defect, to classify the defect by type;
- and
- a remediation subsystem configured to remediate the classified defect.
6 more elements
A method, comprising:
- training an autoencoder using a training UT scan image;
- applying an input noisy ultrasonic test (UT) scan image of a structure to the trained autoencoder, wherein the noisy UT scan image includes noise and a plurality of features, and wherein the noisy UT scan image has a first size;
- compressing the noisy UT scan image using an encoder having a first layer of artificial neurons to generate an encoded representation having a second size at least two orders of magnitude smaller than the first size;
- responsive to the encoded representation, passing only the plurality of features as an output using a bottleneck having a second layer of artificial neurons;
- decompressing the output of the bottleneck to generate a de-noised UT scan image using a decoder having a third layer of artificial neurons;
- applying the de-noised UT scan image to a support vector machine (SVM);
- automatically determining by the SVM, without user input, whether the structure has a defect or does not have a defect from the de-noised UT scan image;
- responsive to the automatic classification of the structure as having a defect, classifying the defect as to a type of defect using a convolutional neural network (CNN);
- and
- remediating the classified defect using a remediation subsystem.
7 more elements
Dependencies are inferred from the claim text.