Ultrasonic Flaw Detection in Turbine Rotor Disc Keyway Using Neural Network

A number of stress corrosion cracks in turbine rotor disk keyway in power plants have been found and the necessity has been raised to detect and evaluate the cracks prior to the catastrophic failure of turbine disk. By ultrasonic RF signal analysis and using a neural network based on bark-propagation algorithm, we tried to evaluate the location, size and orientation of cracks around keyway. Because RF signals received from each reflector have a number of peaks, they were processed to have a single peak for each reflector. Using the processed RF signals, scan data that contain the information on the position of transducer and the arrival time of reflected waves from each reflector were obtained. The time difference between each reflector and the position of transducer extracted from the scan data were then applied to the back-propagation neural network. As a result, the neural network was found useful to evaluate the location, size and orientation of cracks initiated from keyway.