Pressure tube inspection within CANDU nuclear reactors is a critical maintenance operation to identify and track the growth of defects. Current inspection approaches utilising ultrasonic techniques are technically challenging due to transducer alignment caused by the tube dimensional changes. This Thesis focuses on enhancing ultrasonic techniques to improve the inspection accuracy by introducing signal processing algorithms and phased array technology. This work is motivated by the nuclear industry desire to reduce the time and cost consuming replica processes.The Synthetic Aperture Focusing Technique (SAFT) has been applied to industrial inspection data where the ultrasonic image performance is poorly-focused. The transducer focal point operates as a virtual source to transmit ultrasound with a corresponding beam angle. Subsequently, the refocused image demonstrates a distinct improvement in the measurement of defect width.Regarding to the defect depth measurement, this Thesis proposes a wavelet analysis method, which employs the Haar wavelet to decompose the original poorly-focused A-scan signal and reconstruct the defect information from selected frequency components within the transducer operational bandwidth. Compared to the original image characterisation, this method provides an improved estimate of defect depth within an acceptable error ±0.04 mm.A hybrid simulation platform for ultrasonic phased array transducer inspection has been developed and experimentally validated, which combines the benefits of finite element modelling and analytical extrapolation. This approach has been used to study a range of phased array imaging solutions based on both the Total Focusing Method and array SAFT processing.The phased array technique is predicted to improve the accuracy of characterising defects on the inner and outer surfaces of the pressure tube and a dual array system incorporating 32-element 5 and 10 MHz arrays is proposed as a potential future sensor head configuration. The results conclude there is significant potential to improve the quality of the inspection data.
|Date of Award||22 May 2019|
- University Of Strathclyde
|Sponsors||University of Strathclyde|
|Supervisor||Anthony Gachagan (Supervisor) & Gordon Dobie (Supervisor)|