Baker Hughes Oilfield Operations LLC

Baker Hughes Holdings LLC

Sheldon McCrackin

Jeremy Vandam

Carlneil Domkam

Samvid Jhaveri

Kevin Andrew Coombs

Brice Kim

Tom Lambdin

Braden Starcher

A method of nondestructive testing includes receiving data characterizing an image of an inspection region of an industrial machine acquired by an inspection device configured to inspect the inspection region. The inspection device includes a camera and a light source. The camera has a first position and a first orientation and the light source has a second position and a second orientation when the image is acquired. The method also includes identifying a defect in the inspection region of the industrial machine based on the received data characterizing the image of the inspection region. The method further includes determining that a new image of the inspection region needs to be acquired. The method also includes varying one or more of position of the camera, orientation of the camera, position of the light source and orientation of the light source.

Automated defect detection

An inspection system is provided and includes a camera and controller. The controller can include one or more processors in communication with the camera and receive a plurality of images of a target captured by the camera. The controller can also determine, using a first computer vision algorithm, a first prediction and corresponding confidence level for substantially all of the images. The controller can select a subset of the images having the first prediction confidence level greater than or equal to a first prediction threshold value. The controller can additionally determine, using a second computer vision algorithm, a second prediction and corresponding second prediction confidence level for each of the selected images. The at least one second prediction can require more time to determine than the at least one first prediction. The controller can output the second prediction and the second prediction confidence level for each of the selected images.

Computer vision inferencing for non-destructive testing

An inspection system is provided and includes a camera and controller. The camera can acquire at least one image of a target including opposed first and second surfaces. The controller can be in communication with the camera and receive the at least one image. The controller can also detect, using at least one computer vision algorithm, a geometry of the target including the first target surface and the second target surface of the target within at least one image. The at least one image can be acquired at a respective time under respective operating conditions. The controller can additionally segment erosion within the at least one image using the at least one computer vision algorithm. The controller can also generate an erosion depth profile for the at least one image. The erosion depth profile can characterize a depth of erosion of the target between the first and second surfaces.

Erosion detection and prediction

A method of adaptive inspection includes receiving data characterizing one or more images of an inspection region of an industrial machine acquired by an inspection system operating based on a first set of operating parameters. The inspection region includes a site feature. The method also includes determining, by an analytical model, one or more characteristics of the inspection region from the received data characterizing the one or more images of the inspection region. The method further includes generating a control signal based on the one or more characteristics of the inspection region and/or a user input. The inspection system is configured to perform a new inspection of the inspection region based on the control signal.

Adaptive borescope inspection

Systems and methods for target inspection are provided. The system includes a camera, at least one sensor, and a controller. The camera acquires images of a target over time and the sensor(s) acquire motion data characterizing camera and target movement. The controller generates, using a first computer vision (CV) algorithm, an initial prediction and confidence level regarding an object of interest for a first image acquired at a first time. The controller also determines, using the motion data, a motion parameter characterizing relative motion between the camera and the target at the first time. The controller additionally receives a weighting based upon a second image acquired at a second time prior to the first time. The controller generates, using a second CV algorithm, a final prediction and confidence level for the first image based upon the first image, the initial prediction and confidence level, the motion parameter, and the weighting.

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Houston, TX, United States of America

Sheldon McCrackin