Application of Bandpass Filtering in Ultrasonic Non-Destructive Testing

Abstract

LNTRODUCTIONUltrasonie nondestructive testing oflarge grained materials is limited by the ability of the detection process to distinguish the flaw signals from the backscattered grain boundary echoes. This coherent grain noise often masks the echo from inhomogeneities and defects in the material. Absorption and scattering effects further reduce the ultrasound energy leading to poor signal-to-noise ratio in the received signal. It is not possible to reduce the grain clutter by conventional time averaging techniques due to its coherent nature. Different algorithms utilizing the principles of frequency diversity and spatial diversity have been used in the past for signal-to-noise ratio enhancement. In NDE applications where the noise is primarily due to Rayleigh scattering, it can be shown that flaw detection can be improved significantly by merely bandpass filtering the lower part of the received wideband echo spectrum. Both theoretical and experimental results are presented to support this conclusion. The filtering technique is successfully tested on materials with different grain sizes. The main advantage of this method is its relative simplicity, which eliminates the need for sophisticated and computationally intensive signal processing algorithms. Furthermore, this technique allows simple hardware implementation for real-time applications. The optimal parameters, i.e., the center frequency and bandwidth of the bandpass filter are experimentally determined.

Coherent Flaw Reflectivity Estimation in Nonstationary Noise

“…where hs(t, 1..) is the impulse response of the pulse-echo system for the unresolvable scatterers located in the neighborhood corresponding to t. This impulse response depends not only on the illuminating pulse and back-scattering characteristics of the unresolvable scatterers, but also on the transmission path up to point t. The two-way transmission path will have a filtering effect on the propagating pulse that depends on grain size and the bandwidth of the illuminating pulse [1][2][3]. The corresponding term for the target, hr(t, 1..), differs from hs(t, 1..) due to the difference in the back-scattering properties [3].…”

“…The corresponding term for the target, hr(t, 1..), differs from hs(t, 1..) due to the difference in the back-scattering properties [3]. For example, if the target scatterer is assumed to be much larger than the noise scatterers, then energy scattered from the target will have a lower frequency content relative to the back-scattered energy from smaller scatterers.…”

“…The adaptive implelllentation is motivated by the nonstationary behavior of the back-scattered energy received over the duration of the A-scan [1]. This nonstationarity results from the frequency dependent absorption, scattering, and diffraction that occurs as the pulse propagates through the material [2,3].…”

“…The adaptive implelllentation is motivated by the nonstationary behavior of the back-scattered energy received over the duration of the A-scan [1]. This nonstationarity results from the frequency dependent absorption, scattering, and diffraction that occurs as the pulse propagates through the material [2,3].The experimental results indicate that the model's covariance matrix characterizes phase information related to the microstructures in the material. While no experiments have been performed to directly demonstrate this conjecture, the MLE performance is compared for cases when the phase elements of the covariance matrix are and are not included in the MLE computation.…”

A New Adaptive Grain Noise Cancellation Filterng Technique

“…In general, grains are randomly distributed and grain noise can be modeled as a stochastic process [2]. Several signal processing techniques have been used to reduce grain noise, including spatial averaging [3], bandpass filtering [4], and split spectrum processing [5]. However, the statistical characteristics of grain noise is spatially varying and consequently the grain noise cancellation filter must adapt to the spatial changes.…”