{'fraction_non_alphanumeric': 0.015228426395939087, 'fraction_numerical': 0.0, 'mean_word_length': 5.721590909090909, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Model-based compressive sensing for damage localization in lamb wave inspection

Compressive sensing (CS) has emerged as a potentially viable technique for the efficient compression and analysis of high-resolution signals that have a sparse representation in a fixed basis. In this work, we have developed a CS approach for ultrasonic signal decomposition suitable to achieve high performance in Lamb-wave-based defect detection procedures. In the proposed approach, a CS algorithm based on an alternating minimization (AM) procedure is adopted to extract the information about both the system impulse response and the reflectivity function. The implemented tool exploits the dispersion compensation properties of the warped frequency transform as a means to generate the sparsifying basis for the signal representation. The effectiveness of the decomposition task is demonstrated on synthetic signals and successfully tested on experimental Lamb waves propagating in an aluminum plate. Compared with available strategies, the proposed approach provides an improvement in the accuracy of wave propagation path length estimation, a fundamental step in defect localization procedures.