ASSAI: Unmanned aerial systems for advanced contact inspection of civil structures
Unmanned aerial systems (UAS) offer companies a potential reliable and safe solution to support their operations by accessing areas that are otherwise too difficult, without extensive manpower and support. Although rapid gains have been made in this field of technology, there remains the need to carry out advanced contact non-destructive testing (NDT) in order for UAS systems to truly become the primary method of facility inspection and monitoring.
Currently, the sensor equipment being produced for use with UAS are limited to a range of imaging equipment such as video cameras through to thermal imaging cameras, surveying and mapping technology. As a result, human inspections coupled with ultrasonic sensing systems are still required in parallel with the UAS system, especially for critical infrastructure inspections.
The ASSAI project aims to develop a functioning ultrasonic inspection unmanned aerial systems (UAS), with fully integrated UT probe capable of performing Under Bridge thickness measurements of steel support beams.
Our project will deliver significant productivity increases for our customers, and provide exciting growth to the SME partners in the project. During the first five years (2021-2025) we will generate cumulative total revenue of £28.3M and cumulative profit of £6.2M, from the sale of 345 ASSAI systems. We estimate a very attractive financial return of 1027% (IRR). Internal-Rate-of-Return is more representative of the longer-term investment than ROI, which does takes into account depreciation/inflation
Brunel Innovation Centre's Role
BIC developed a novel Artificial Intelligence assisted Ultrasonic Signal processing method. It combines the advantages of both conventional signal processing and the deep learning assisted method, enhancing this way the accuracy and reliability of the inspection. Based on the acquired data, an algorithm decides which method can offer higher reliability and accuracy in the output data, and processes the signal accordingly. Actually, Brunel University London developed 3 algorithms:
- Corrosion detection: thanks to the combination of signal processing and machine learning, BUL was able to automate the detection but also to go beyond the accepted sensor standard sensitivity
- Quality of data assessment (standard, substandard) using machine learning. This is critical for drone inspection as it allows to know if a measurement should be repeated or no automatically and in real time without downloading and assessing the data
- Surface finish detection
- Air Control Entech Ltd
- TWI Ltd
- JR Dynamics Ltd
- James Fisher Testing Services Ltd
- Brunel University London
Meet the Principal Investigator(s) for the project
Professor Tat-Hean Gan
- Professional Qualifications - CEng. IntPE (UK), Eur Ing, BEng (Hons) Electrical and Electronics Engg (Uni of Nottingham), MSc in Advanced Mechanical Engineering (University of Warwick), MBA in International Business (University of Birmingham), PhD in Engineering (University of Warwick), Languages - English, Malaysian, Mandarin, Cantonese, Professional Bodies - Fellow of the British Institute of NDT, Fellow of the Institute of Engineering and Technology, Tat-Hean Gan has 10 years of experience in Non-Destructive Testing (NDT), Structural Health Monitoring (SHM) and Condition Monitoring of rotating machineries in various industries namely nuclear, renewable energy (eg Wind, Wave ad Tidal), Oil and Gas, Petrochemical, Construction and Infrastructure, Aerospace and Automotive. He is the Director of BIC, leading activities varying from Research and development to commercialisation in the areas of novel technique development, sensor applications, signal and image processing, numerical modelling and electronics hardware. His experience is also in Collaborative funding (EC FP7 and UK TSB), project management and technology commercialisation.