A piezoelectric coded-excitation scanning acoustic transducer for Lamb wave inspections

Several techniques for Guided Wave (GW) inspections have already been developed. Most of them rely on extensive sensor deployment and damage localization algorithms characterized by significant computational costs. However, there is a growing demand for simpler, more autonomous, and more affordable systems across various fields. In particular, the implementation of wireless, battery-powered systems with a reduced number of sensor nodes and simpler processing would greatly facilitate the transition of this inspection technology on the field. Following this direction, this work presents the design of a novel piezoelectric transducer composed of four different patches, i.e. with only four input/output channels, to scan a given area. The peculiar piezo-load distributions allow the association of different spectral binary sequences for each 6 degrees discrete angular step. By evaluating the distance-of-flights (DoFs) of detected peaks, the range coordinates for multiple defects are identified. Meanwhile, the angular information is extracted by demodulating binary sequences of peaks with comparable DoFs across several frequency bands. Since the transducer is designed as an encoder, it is referred to as coded-excitation scanning acoustic transducer (CESAT). More specifically, the Gray code is used to generate spectral patterns to reduce the uncertainty between two adjacent angular steps. A new quantization procedure for the optimal generation of the piezo distributions is also proposed. Ad hoc signal processing algorithms, suitable for embedded applications, were developed to extract multi-target range and angle information. The processing is based on the frequency decomposition of the recorded signal using an FIR filter bank and on dispersion compensation procedures for pulse 're-compression'. The transducer encoder behavior is validated through a finite element analysis. Finally, numerical simulations were performed to assess the effectiveness of the CESAT and associated signal processing in multi-defect detection and localization tasks.