Grant: Collaborative Research and Development Grants

As a major electricity producer with an ever-increasing demand, it is vital that the Canadian power industry strive to improve the safety, productivity and reliability of its generating stations. In particular, nuclear power plants' safe and continuous operation critically depends on the proper maintenance and inspection of certain surfaces. Non-destructive testing (NDT) is a type of analysis performed to detect fatigue effects and structural imperfections on a finished product while ensuring that it is not damaged or destroyed during testing. This project is concerned with the application of novel sensor array processing and multi-input multi-output (MIMO) imaging techniques to ultrasonic NDT in nuclear power plants. Sensor array processing is a branch of signal processing where the outputs of an array of sensors are collected and processed in order to obtain the image of the surrounding environment. MIMO signal processing has been widely investigated and is well understood as a means to increase reliability and data throughput in the area of wireless connectivity. In this project, we develop novel statistical and array signal processing techniques that enable the detection of various types of abnormalities within the pipes that feed the boiler of a nuclear power plant. To do so, we propose data models which capture the effects of variations in the refraction indices, the calibration errors, signal characteristics, noise properties, and inter-sensor coupling. Our research in non-destructive testing procedures is directed at meeting two industry targets: i) decreasing financial losses incurred during testing procedures by developing faster testing methods and processing algorithms, and ii) reducing costs incurred during the training of specialized safety inspection personnel who have a high-turnover rate due to regulations stipulating the acceptable radiation doses humans can safely sustain. The major economic benefit will be the simultaneous detection of multiple flaw types such as cracks, thinning, and porosity. This will reduce future inspection campaign times during outages, thereby leading to a net economical advantage.