System and method for evaluating residual life of components made of composite materials

A system comprising:

• a memory storing instructions;
• one or more communication interfaces;
• an ultrasound sensor;
• a pulse echo ultrasound sensor;
• a thermal camera;
• and
• one or more hardware processors coupled to the memory via the one or more communication interfaces, wherein the one or more hardware processors are configured by the instructions to:
• transmit, via the ultrasound sensor, a plurality of ultrasound signals towards a test component comprising a defect and receiving the plurality of ultrasound signals reflected by the test component;
• extract a plurality of features comprising time-frequency and statistical features from each of the plurality of received ultrasound signals;
• determine type of the defect in the test component using a pre-trained classifier based on the plurality of features, wherein training of the pre-trained classifier comprises:
• performing ultrasound test on a component without defect and a plurality of components with known defects, wherein the ultrasound test comprises transmitting a plurality of ultrasound signals to the component and recording the plurality of ultrasound signals reflected by the component;
• extracting a plurality of features from the results of the ultrasound test;
• correlating the plurality of features with the known defects of the plurality of components;
• and
• training the classifier using the plurality of features and corresponding defects;
• determine a location of the defect comprised in the test component based on time of flight analysis of the plurality of ultrasound signals, wherein the time of flight of the ultrasound signal is time taken by the ultrasound signal to travel a certain distance through the test component, wherein the location of the defect is determined by minimizing an objective function J=Σj−1Nf(j)2, wherein
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• wherein LST−D is distance between transmitter of the ultrasound sensor and the defect which is calculated as √{square root over (xD2+yD2)} wherein (xD, yD) is the location of the defect, V is velocity of an ultrasound signal among the plurality of ultrasound signals, LD−Sj is distance between the defect and a receiver (j) among one or more receivers of the ultrasound sensor which is calculated as √{square root over ((xD−xj)2+(yD−yj)2)} wherein (xj, yj) is location of the receiver (j), LST−Sj is distance between the transmitter and the receiver (j) which is calculated as √{square root over (xj2+y2)}, and ΔT−j is difference in time of flight of the ultrasound signal travelled via a direct path and an indirect path from the transmitter to the receiver;
• scan, via the pulse echo ultrasound sensor, the location of the defect in the test component using a pulse echo ultrasound signal to determine depth of the defect;
• scan, via the thermal camera, the location of the defect in the test component to estimate a dimension of the defect;
• and
• predict residual life of the test component using a pre-trained machine learning model based on the type of the defect, the location of the defect, the depth of the defect and the dimension of the defect, wherein training of the pre-trained machine learning model comprises:
• obtaining residual properties of a plurality of components with known defects, wherein the residual properties comprise stiffness and load bearing capacity;
• determining residual life of each of the plurality of components using a material degradation mechanism and a computational method based on the defect in the component, one or more predefined loading conditions, and material properties of the component;
• and
• training a machine learning model to generate the pre-trained machine learning model using the residual life of the plurality of components, features of defects in the plurality of components for the one or more predefined loading conditions and the material properties of the plurality of components, wherein the features of defects comprise type of the defect, location of the defect, depth of the defect and dimension of the defect.

One or more non-transitory machine-readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors cause:

• transmitting, via an ultrasound sensor, a plurality of ultrasound signals towards a test component comprising a defect, and receiving the plurality of ultrasound signals reflected by the test component;
• extracting a plurality of features comprising time-frequency and statistical features from each of the plurality of received ultrasound signals;
• determining type of the defect in the test component using a pre-trained classifier based on the plurality of features, wherein training of the pre-trained classifier comprises:
• performing ultrasound test on a component without defect and a plurality of components with known defects, wherein the ultrasound test comprises transmitting a plurality of ultrasound signals to the component and recording the plurality of ultrasound signals reflected by the component;
• extracting a plurality of features from the results of the ultrasound test;
• correlating the plurality of features with the known defects of the plurality of components;
• and
• training the classifier using the plurality of features and corresponding defects;
• determining a location of the defect comprised in the test component based on time of flight analysis of the plurality of ultrasound signals, wherein the time of flight of the ultrasound signal is time taken by the ultrasound signal to travel a certain distance through the test component, wherein the location of the defect is determined by minimizing an objective function J=Σj−1Nf(j)2, wherein
• f
• ⁡
• (
• j
• )
• =
• (
• L
• S
• T
• -
• D
• V
• +
• L
• D
• -
• S
• j
• V
• )
• -
• L
• S
• T
• -
• S
• j
• V
• -
• Δ
• ⁢
• t
• T
• -
• j
• =
• 0
• ,
• wherein LST−D is distance between transmitter of the ultrasound sensor and the defect which is calculated as √{square root over (xD2+yD2)} wherein (xD, yD) is the location of the defect, V is velocity of an ultrasound signal among the plurality of ultrasound signals, LD−Sj is distance between the defect and a receiver (j) among one or more receivers of the ultrasound sensor which is calculated as √{square root over ((xD−xj)2+(yD−yj)2)} wherein (xj, yj) is location of the receiver (j), LST−Sj is distance between the transmitter and the receiver (j) which is calculated as √{square root over (xj2+yj2)}, and ΔtT−j is difference in time of flight of the ultrasound signal travelled via a direct path and an indirect path from the transmitter to the receiver;
• scanning, via a pulse echo ultrasound sensor, the location of the defect in the test component using a pulse echo ultrasound signal to determine depth of the defect;
• scanning, via a thermal camera, the location of the defect in the test component to estimate a dimension of the defect;
• and
• predicting residual life of the test component using a pre-trained machine learning model based on the type of the defect, the location of the defect, the depth of the defect and the dimension of the defect, wherein training of the pre-trained machine learning model comprises:
• obtaining residual properties of a plurality of components with known defects, wherein the residual properties comprise stiffness and load bearing capacity;
• determining residual life of each of the plurality of components using a material degradation mechanism and a computational method based on the defect in the component, one or more predefined loading conditions, and material properties of the component;
• and
• training a machine learning model to generate the pre-trained machine learning model using the residual life of the plurality of components, features of defects in the plurality of components for the one or more predefined loading conditions and the material properties of the plurality of components, wherein the features of defects comprise type of the defect, location of the defect, depth of the defect and dimension of the defect.