Exploratory analysis of sub-terahertz sensor data characteristics for the purposes of machine learning technique development

Corrosion Under Insulation (CUI) on pipework, and the moisture that causes it, are significant challenges faced by the energy and processing industries.

SubTera has recently developed a game-changing pipework inspection capability, and over the past 18 months, has been field-testing a prototype inspection tool in collaboration with global energy producers.

There are numerous ways in which SubTera's technology can contribute to helping the energy industry achieve its net-zero objectives. According to Norway's Petroleum Safety Authority, 50% of reported hydrocarbon leaks at onshore plants are caused by CUI. The primary function of SubTera's technology is to detect CUI and moisture at the earliest onset, which will reduce the risk of, and cost associated with, pipeline failure.

This has the potential to save millions of pounds of new infrastructure costs, clean-up fees, and fines; however, the associated reduction in leakage of fugitive emissions will further reduce our carbon footprint.

As part of the global transition to a net-zero energy world, coupled with the evolution toward Industry 4.0, SubTera acknowledges that in the future, its sensor technology must be integrated within robotic platforms, and inspections will be conducted autonomously. Over the next 24 months, SubTera plans to develop a new system, that incorporates machine learning and enables robotic integration. The exploratory mini-project proposed herein is a critical first step in enabling that future.

During this mini-project, a number of existing inspection data sets, captured using SubTera's TRL7 prototype, will be analysed (i.e.: identifying data trends, study patterns and variation, removing sensor noise, understanding errors and uncertainties). Through this analysis, the characteristics of SubTera's sensors will be determined, and the requirements to enable efficient machine learning integration will be identified.

The output from this project will be a series of insights, presented in a report, to guide future system design (i.e.: sensor, optical), system operation, and the implementation of machine learning techniques within SubTera's next development phase.