Artificial intelligence in NDT
When I hear the term ‘artificial intelligence (AI)’, I have tended to cover my eyes and my ears and comfort myself by saying that it is a ‘brave new world’; I am close to the end of my non-destructive testing (NDT) career and I do not have to deal with it. However, there seems no escaping it, so I researched information about its value to NDT.
I found an article by Peter Rosiepen, Managing Director of DIMATE GmbH, which gave me a primer in its application to NDT. Peter Rosiepen is a visionary in the field of industrial NDT digitalisation.
He explains that the field of NDT is undergoing a transformation with the emergence of artificial intelligence. The ability of AI to automate tasks and identify patterns is being increasingly harnessed to improve the efficiency and accuracy of the inspection process. Peter’s article explores the applications and limitations of AI in NDT, as well as the role of a picture archiving and communication system (PACS) in supporting AI-based inspection.
One of the main applications of AI in NDT is the automatic recognition of components and the assignment to an inspection instruction. The software can recognise the inspection part based on the images and automatically place a matching template over the image, which shows exactly where measurements must be taken. The generated inspection data is then fed back into the inspection data management
system (IDMS).
This automated inspection can also be carried out retroactively for inspections that have already been performed, for example for quality assurance purposes. AI can analyse past inspection data and identify patterns and trends that can help improve future inspections. By leveraging historical data, AI can help identify areas that require more attention and detect potential issues before they become major problems.
Another application of AI in inspection is the automated detection of erosion, corrosion and deposits on the test images. This means that the AI system can analyse the images captured during the inspection process and identify corresponding signs without requiring the inspector to manually review each image. This not only improves the efficiency of the inspection process but also enhances the accuracy of the detection, as AI systems can often identify subtle defects that may be missed by human inspectors. Additionally, AI can be used to automate the measurement process. AI algorithms can detect the point on a pipeline where the wall thickness is the thinnest and measure it automatically.
While AI has the potential to significantly improve NDT, there are still several limitations regarding its implementation. One of the main challenges is the need for appropriate data to develop accurate AI models. Obtaining such data can be difficult, as it requires a structured digital dataset with a wide range of defect types, sizes and orientations. Additionally, human expertise is still required to interpret and make decisions based on the AI results, as AI is rather aimed at supporting inspectors in conducting more accurate and efficient inspections, not their replacement.
The prerequisite for the use of AI solutions is a high-quality database. To build one, the inspection workflow should be digitised in such a way that inspection data and reports are available digitally and there are no media breaks in the process. A PACS fulfils these criteria as it connects leading inspection systems, such as enterprise resource planning (ERP) and risk-based inspection (RBI), and streamlines the entire process, from NDT data acquisition to evaluation, management and archiving. Therefore, the software provides an excellent database for organisations to train AI models and profit from using them in NDT and inspection.
Peter concludes by stating that AI has the potential to significantly improve and optimise NDT. The benefits include increased productivity, better accuracy and faster inspection times. The key to unlocking the potential of AI in NDT is to implement the necessary technologies to digitise inspection workflows and create a structured digital database. Therefore, it is time for organisations to take further steps towards a more advanced and optimised NDT and utilise the possibilities that AI offers. A PACS is a prerequisite for AI-based inspection as it provides a high-quality database to train AI models, which can then be used to improve inspection processes.
OK, I have a better understanding of the potential benefits that AI offers. I am now ready to absorb the benefits of AI when applied to NDT; I am anxious to obtain more knowledge and become aware of any negative effects and to what extent AI processes will replace the type of quality information presently accrued by the Level 2 technician.
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