Time domain deconvolution approach relying on Galois sequences

Abstract

Purpose

The paper seeks to present a novel pulsed eddy currents (PEC) non‐destructive technique to investigate deep cracks in metallic structures.

Design/methodology/approach

The method is based on the time‐domain analysis of the “defect response” and joins the PEC approach with the exploitation of the peculiar auto‐correlation properties of the Galois sequences. The procedure, relying on the deconvolution of Galois sequences, greatly improves the signal to noise ratio (SNR) and thus the operating depth. De‐convolving even short Galois sequences allows one to investigate at depth ranges larger than those allowed by the conventional pulsed excitation techniques.

Findings

The technique has been tested on a benchmark and compared with numerical simulations. The experimental results showed that the SNR and the detection depth range have been significantly improved.

Research limitations/implications

Some limitations of the measuring set up were evidenced requiring a new measuring apparatus if explorations at larger depths are of interest: the 0.1 per cent impedance differences among the four coils in the bridge, although limited by an accurate construction, resulted in a limitation of the measuring system in DSP procedure adopted to null the background signal: different probe configurations must be pursued in order to allow further improvements in the deep defect detection.

Originality/value

For the first time the peculiar Galois sequences excitation was applied in a PEC system. By using these inputs the signal energy was significantly enhanced, allowing one to reconstruct by the deconvolution process the crack signatures.

Citation

Burrascano, P., Carpentieri, M., Pirani, A., Ricci, M. and Tissi, F. (2007), "Time domain deconvolution approach relying on Galois sequences", COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, Vol. 26 No. 2, pp. 380-388. https://doi.org/10.1108/03321640710727737