Principal Investigator: Prof Paul Wilcox

Summary

This pilot project will apply data science to exploit the full potential of quantitative Non-Destructive Evaluation (NDE) measurements of engineering assets, ranging from power stations to aeroplanes. Currently, the majority of NDE data is irreversibly condensed after a measurement is made, sometimes to as little as a binary pass/fail classification. In doing so, a wealth of useful information that could impact on the future safety and economic utility of an asset is lost forever. However, as the fourth industrial revolution unfolds, the enabling infrastructure to store raw NDE data from every inspection is becoming available in the form of scalable, cloud-based, asset-management platforms.

Permanent storage of raw NDE measurement data will become the norm, as it future-proofs historic measurements against new processing techniques as they become available. From a data science perspective this also presents a massive opportunity to extract much more information about the integrity of an engineering asset. NDE data from multiple measurement modalities can be integrated and analysed collectively in the context of historic measurements and data streams from other condition-monitoring modalities.

The specific objectives of the pilot project are to: 

1. Create the foundations of NDE data science by developing the mathematical taxonomy of ‘big’ NDE data to the point where the challenges can be clearly articulated to the wider data science community.
2. Promote, through two workshops, the value of applying data science to NDE to both the industrial end-user community (e.g. Airbus, Rolls-Royce, EDF, Hitachi, BP) and data scientists.
3. Develop one or more case studies with an industrial end-user that will demonstrate the value of NDE data science.