Title

Early Detection of Wire Fracture in 7-Wire Strands through Multiband Wavelet Analysis of Acoustic Emission Signals

Abstract

In this study, acoustic emission (AE) features to predict and detect wire fracture in seven-wire strands were characterized with multiband wavelet analysis. Two steel strands were tested up to 89 kN with each instrumented with a pair of AE sensors at two ends. The cross section of one wire was locally reduced up to 90% in 10% increment at center and support of the two strands, respectively. For both strands, the AE parameters (hits, energy, and counts) changed little up to 80% reduction in cross section of the partially cut wire, and suddenly jumped at the fracture (under 73 kN) of the notched wire with 90% reduction in cross section. The acoustic signals of inter-wire slippage and fracture initiation are significantly shorter in time duration than the signal of fracture. Their dominant frequencies and frequency bandwidths are increasingly higher and wider. The frequency band of the fracture signal is significantly broader than that of either the fracture-induced echo or artificial tapping noises. The time duration of artificial tapping noises is substantially longer than that of either fracture or fracture-induced echo. These distinct time-frequency characteristics allow an early detection and localization of wire fracture following the proposed procedure.

Department(s)

Civil, Architectural and Environmental Engineering

Research Center/Lab(s)

INSPIRE - University Transportation Center

Comments

Financial support for this study was provided in part by the National Science Foundation under Award No. CMMI - 1538416 and by the Department of Civil, Architectural, and Environmental Engineering at Missouri University of Science and Technology.

Keywords and Phrases

Acoustic emission; Fracture detection; Fracture localization; Noise characterization; Time-frequency analysis; Wavelet transform

International Standard Serial Number (ISSN)

0141-0296

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2020 Elsevier Ltd, All rights reserved.

Publication Date

01 Mar 2020

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