Probability of detection of lifecycle phases of corrosion under insulation using artificial intelligence and temporal thermography

Patent metadata

Field	Value
Publication number	US-2024053287-A1
Application number	US-202318365612-A
Country	US
Kind code	A1
Filing date	Aug 4, 2023
Priority date	Aug 12, 2022
Publication date	Feb 15, 2024
Grant date	—

Abstract

A system for determining corrosion under insulation of an industrial asset is provided. The system includes an infrared camera configured to acquire one or more time-series infrared images of an industrial asset. The system further includes a computing device configured to receive data characterizing the one or more time-series infrared images, and to identify an area of interest of the industrial asset within the one or more time-series infrared images. The computing device further configured to identify, by a machine learning algorithm, a plurality of defects within the area of interest based on pixel-wise assignment of at least one defect category selected from a plurality of defect categories associated with corrosion under insulation of the industrial asset, and to provide the plurality of defects within the area of interest of the industrial asset. Related methods, apparatuses, and computer-readable mediums are also provided.

First claim

1 . A system comprising:
an infrared camera configured to acquire one or more time-series infrared images of an industrial asset; a computing device including at least one data processor, and a memory coupled to the at least one data processor and storing instructions, which when executed, cause the at least one data processor to perform operations comprising:
receiving data characterizing the one or more time-series infrared images of the industrial asset,
determining an area of interest of the industrial asset within the one or more time-series infrared images,
determining, by a machine learning algorithm, a plurality of defects associated with pixels within the area of interest, wherein each defect of the plurality of defects is determined based on pixel-wise assignment of at least one defect category selected from a plurality of defect categories for each pixel of the one or more time-series infrared images and each defect is represented by a cluster of pixels in which each pixel is assigned an identical defect category, and wherein each defect category is associated with a lifecycle of corrosion under insulation of the industrial asset, and
providing the determined plurality of defects within the area of interest in the one or more time-series infrared images of the industrial asset.

2 . The system of claim 1 , wherein the plurality of defect categories includes a defect-free category, an insulation damage category, a moisture accumulation category, a metal corrosion category, and a deep-metal loss corrosion category, and further wherein the lifecycle of corrosion under insulation associated with each defect category includes a sequence of progressive stages of corrosion of the industrial asset.

3 . (canceled)

4 . The system of claim 1 , wherein the instructions are further configured to cause the at least one data processor to train the machine learning algorithm by performing operations comprising:
receiving data characterizing one or more time-series infrared images of the industrial asset acquired via an infrared camera; annotating the one or more time-series infrared images with ground-truth annotations based on physical examination of the industrial asset; and training the machine learning algorithm based on the annotated one or more time-series infrared images.

5 . The system of claim 1 , wherein the instructions are further configured to cause the at least one data processor to train the machine learning algorithm by performing operations comprising:
receiving data characterizing one or more training configuration parameters associated with at least one defect category selected from the plurality of defect categories and associated with the lifecycle of corrosion under insulation of the industrial asset; generating a plurality of defect image patches based on the data characterizing one or more training configuration parameters, the plurality of defect image patches including the defect; overlaying one or more of the defect image patches onto defect-free time-series image data of the industrial asset, the defect-free time-series image data devoid of any defects of the industrial asset; generating time-series image training data based on the overlaying, wherein the generated time-series image training data comprises ground-truth annotations corresponding to one or more defect categories, the ground-truth annotations determined based on the one or more training configuration parameters; and training the machine learning algorithm using the generated time-series image training data.

6 . The system of claim 1 , wherein the instructions are further configured to cause the at least one data processor to train the machine learning algorithm by performing operations comprising:
receiving field-originated time-series infrared images of the industrial asset acquired via an infrared camera and annotated with ground-truth annotations based on physical examination of the industrial asset; receiving time-series image training data generated based on overlaying one or more defect image patches onto defect-free time-series image data of the industrial asset, the defect-free time-series image data devoid of any defects of the industrial asset, wherein the time-series image training data comprises ground-truth annotations corresponding to one or more defect categories; and training the machine learning algorithm based on a combined training dataset including the field-originated time-series infrared images and the generated time-series image training data.

7 . The system of claim 5 , wherein the data characterizing one or more training configuration parameters further include a surface temperature associated with the industrial asset, a temperature of a fluid within the industrial asset, a type of defect, a size of a defect, a shape of a defect, a depth of a defect, a location of a defect, a metal thickness of the industrial asset, a metal type of the industrial asset, or a thickness of the insulation.

8 . (canceled)

9 . (canceled)

10 . (canceled)

11 . (canceled)

12 . The system of claim 5 , wherein the data characterizing one or more training configuration parameters includes a defect depth or a defect size, and generating the plurality of defect image patches further comprises:
determining, using a first physical model of temperature propagation through a cross-section of the industrial asset, at least one temperature profile of the industrial asset; generating, based on the defect depth or the defect size and the determining a surface temperature for each pixel included in the plurality of detect image patches; and providing the surface temperature for each pixel in the plurality of defect image patches, wherein the surface temperature is provided in the plurality of defect image patches as a cross-sectional view of the industrial asset.

13 . The system of claim 5 , wherein the data characterizing one or more training configuration parameters includes a defect location corresponding to a corrosion origination point, and generating the plurality of defect image patches further comprises:
determining, using a second physical model of temperature propagation across a surface of the industrial asset, at least one surface temperature profile of the industrial asset; generating, based on the defect location and the determining, a surface temperature distribution within the plurality of defect image patches; and providing the surface temperature distribution in the plurality of defect image patches, wherein the surface temperature distribution extends across the surface of the industrial asset from the defect location corresponding to the corrosion origination point toward edges of the plurality of defect image patches.

14 . The system of claim 13 , wherein generating the plurality of defect image patches further comprises applying a camera noise model corresponding to the infrared camera to the plurality of defect image patches.

15 . (canceled)

16 . (canceled)

17 . A method comprising:
receiving, by a data processor, data characterizing one or more time-series infrared images of an industrial asset, the one or more time-series images acquired via an infrared camera; determining, by the data processor, an area of interest of the industrial asset within the one or more time-series infrared images; determining, by the data processor, a plurality of defects associated with pixels within the area of interest using a machine learning algorithm, wherein each defect of the plurality of defects is determined based on pixel-wise assignment of at least one

Assignees

• Saudi Arabian Oil Co
• Baker Hughes Holdings Llc

Inventors

• Aljarro Ahmed
• Al Shehri Ali
• Amer Ayman
• Althobaiti Abdulrahman
• Saiari Hamad
• Asfoor Fadhel
• Alsubhi Yasser
• Hunter Rick
• Roy Arjun
• Shapiro Vladimir
• Qian Weiwei
• Saad Bilal
• Odisio Matthias
• Chan Godine Kok Yan
• Kairiukstis Edvardas
• Kakpovbia Anthony
• Albaqshi Muntathir
• Alsalman Fatima

Classifications

• G06V10/762

  using clustering, e.g. of similar faces in social networks · CPC title

• G06V2201/07

  Target detection · CPC title

• G06V2201/06

  Recognition of objects for industrial automation · CPC title

• G06V20/52

  Surveillance or monitoring of activities, e.g. for recognising suspicious objects (recognising microscopic objects G06V20/69) · CPC title

• G06V10/82

  using neural networks · CPC title