The performance of ultrasonic non-destructive evaluation (NDE) systems is frequently limited by backscattering and electronic noise, which reduce sensitivity and resolution, particularly in industrial environments. At the Metals Industry Development Institute in Ethiopia, the ultrasonic NDT flaw detector (model UFD-01/T) has been employed to defects internal material defects. However, accurately pinpointing crack related frequencies remains difficult due to substantial environmental noise interference. To address this challenge, a post-processing noise cancellation system was developed utilizing advanced adaptive filtering techniques. This study presents mathematical models for noise reduction and evaluates the effectiveness of several adaptive filter algorithms, including Fast Fourier Transform (FFT), Finite Impulse Response (FIR), Infinite Impulse Response (IIR), and Least Mean Square (LMS) methods. These algorithms were implemented and simulated within the MATLAB environment to assess their ability to isolate defect signals from noise. Simulation results demonstrate that the proposed adaptive filtering methods, particularly the LMS algorithm, effectively attenuate high-frequency noise originating from echoes and environmental interferences. Consequently, the noise floor in the processed ultrasonic signals was reduced to below 35 dBm, significantly enhancing the capability to localize material defects. These findings support the integration of adaptive filtering techniques in ultrasonic NDE systems to improve defect detection precision in noisy industrial environments, thereby enhancing inspection reliability, reducing false positives, and contributing to the overall safety and efficiency of industrial operations.
Yenealem et al. (Mon,) studied this question.