Smart body for contactless detection of defects using artificial intelligence

A non-transitory computer-readable storage medium coupled to one or more hardware processors and storing programming instructions for execution by the one or more hardware processors, the programming instructions instructing the one or more hardware processors to perform operations in a pipe carrying a production fluid, the pipe comprising a body made of one or a combination of metallic, non-metallic, or composite material, the operations comprising:

• determining an induced signal value for each of the plurality of EM receivers;
• receiving the induced signal value for each of the plurality of EM receivers as an input for a neural network tangibly embodied on the non-transitory computer-readable storage medium;
• creating a visual representation of the body based on a neural network comparison of the induced signal values for each of the plurality of EM receivers;
• and
• determining whether a defect exists on the body based on a comparison of the induced signal values for each of the plurality of EM receivers performed by the neural network.

A method performed by a smart body, the method comprising:

• in a pipe carrying production fluid, the pipe comprising a body made of one or a combination of metallic, non-metallic, or composite material:
• transmitting a first electromagnetic (EM) signal from an EM transmitter encased within the smart body towards a body of the pipe;
• receiving, by an EM receiver encased within the smart body adjacent to the EM transmitter, a second EM signal reflected by the body of the pipe, the second EM signal being a reflected version of the first EM signal;
• receiving, by an EM receiver encased within the smart body adjacent to the EM transmitter, a third EM signal reflected by the body of the pipe, the third EM signal being a reflected version of the first EM signal;
• inducing a first induced signal within the EM receiver from the second EM signal;
• inducing a second induced signal within the EM receiver from the third EM signal;
• determining a first value of the first induced signal and determining a second value of the second induced signal;
• inputting the first value and the second value into a physics-driven machine learning algorithm;
• and
• determining the presence of a defect in the body based on a comparison of the first value and the second value by the machine learning algorithm.