Datasets necessary for detection and characterisation of defects in industrial structures are more and more often collected using phased arrays of ultrasonic transducers. Some NDT (Non-Destructive Testing) inspections utilising such arrays are already automated but interpretation of data they collect is not. Automation of data interpretation is desirable, because industry anticipates a severe shortage of suitably qualified and experienced personnel and because there is a pressure for both speeding the NDT inspections up and increasing their reliability.

The approach best suited for utilising array data seems to be TFM (Total Focusing Method). This creates visual images of sections of inspected components. When they are contaminated by noise interpretation of such images is not straightforward. Research engineers began to explore the possibility of automating interpretation of NDT data by utilising machine learning techniques. At present, this approach has limited value: firstly, machine learning techniques rely on big data while there are no repositories of big NDT data; and secondly, conclusions reached by modern AIs are frequently unexplainable while safety critical industries, such as nuclear are unlikely to adopt interpretation tools of this nature. Sound Mathematics Ltd. has been working on an alternative solution, an application software that combines a signal processing algorithm based on a simple modification of a TFM with OpenCV image processing algorithms and a decision tree - an AI (Augmented Intelligence) module, which mimicks thought processes employed by human inspectors in writing inspection reports.

Compared to machine learning algorithms, Decision Trees have two well-known advantages and one disadvantage: they need orders of magnitude fewer datasets for training, they produce explainable results, but developmental challenge they present is huge. To use AutoNDE as an example, the application relies on about 40 different parameters. It took our researchers years to zero-in on the set of parameters that appear to work well in a variety of configurations.

If brought to successful conclusion, the proposed project would decrease operating costs and increase safety of nuclear plants, our initial target market. At a later stage the technology would be transferred to offshore wind turbines. The project is highly timely, in view of the current drive to reduce the world's dependence on oil and gas. Later still the technology can be applied to NDT of various pressure vessels, rails, steel plates used in shipbuilding industry, bolts _etc_. It would reduce the underlying operating costs and increase safety of these industries too.