Prognosis of Wire Fracture through Adaptive Wavelet Analysis of Acoustic Signals


In this study, acoustic emission (AE) features were characterized with adaptive wavelet analysis and used to predict and detect wire fracture in a seven-wire strand that was instrumented with a pair of AE sensors at two ends and tested up to 89 kN. The cross section of one wire was locally reduced up to 90% in 10% increment at center. 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. 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.

Meeting Name

8th International Conference on Structural Health Monitoring of Intelligent Infrastructure, SHMII 2017 (2017: Dec. 5-8, Brisbane, Australia)


Civil, Architectural and Environmental Engineering

Keywords and Phrases

Acoustic noise; Fracture; Signal analysis; Structural health monitoring; Wavelet analysis; Wire; Acoustic signals; Adaptive wavelets; Detection and localization; Fracture initiation; Time duration; Time frequency characteristics; Wire fractures; Wire strands; Acoustic emission testing

International Standard Book Number (ISBN)


Document Type

Article - Conference proceedings

Document Version


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© 2017 International Society for Structural Health Monitoring of Intelligent Infrastructure (ISHMII), All rights reserved.

Publication Date

01 Dec 2017