Deep learning algorithm for supervision process in production using acoustic signal

Safaei, Mahmood; Soleymani, Seyed Ahmad; Safaei, Mitra; Chizari, Hassan; Nilashi, Mehrbakhsh

doi:10.1016/j.asoc.2023.110682

Cited by 5 publications

(1 citation statement)

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“…Apart from the signal and frequency spectrum, this 2D matrix is used to classify the measured signals based on their selected class. A similar approach can be seen in the most recent publications [39][40][41]. In the presented paper, a Python library for fast continuous wavelet transformation was used and published by Arts, Lukas P. A.…”

Section: Impact-echomentioning

confidence: 94%

Non-Destructive Characterization of Cured-in-Place Pipe Defects

Dvořák,

Jakubka,

Topolář

et al. 2023

Materials

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Sewage and water networks are crucial infrastructures of modern urban society. The uninterrupted functionality of these networks is paramount, necessitating regular maintenance and rehabilitation. In densely populated urban areas, trenchless methods, particularly those employing cured-in-place pipe technology, have emerged as the most cost-efficient approach for network rehabilitation. Common diagnostic methods for assessing pipe conditions, whether original or retrofitted with-cured-in-place pipes, typically include camera examination or laser scans, and are limited in material characterization. This study introduces three innovative methods for characterizing critical aspects of pipe conditions. The impact-echo method, ground-penetrating radar, and impedance spectroscopy address the challenges posed by polymer liners and offer enhanced accuracy in defect detection. These methods enable the characterization of delamination, identification of caverns behind cured-in-place pipes, and evaluation of overall pipe health. A machine learning algorithm using deep learning on images acquired from impact-echo signals using continuous wavelet transformation is presented to characterize defects. The aim is to compare traditional machine learning and deep learning methods to characterize selected pipe defects. The measurement conducted with ground-penetrating radar is depicted, employing a heuristic algorithm to estimate caverns behind the tested polymer composites. This study also presents results obtained through impedance spectroscopy, employed to characterize the delamination of polymer liners caused by uneven curing. A comparative analysis of these methods is conducted, assessing the accuracy by comparing the known positions of defects with their predicted characteristics based on laboratory measurements.

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Section: Impact-echomentioning

confidence: 94%