A novel closure based approach for fatigue crack length estimation using the acoustic emission technique in structural health monitoring applications

Abstract

Use of Acoustic Emission (AE) for detecting and locating fatigue cracks in metallic structures is widely reported but studies investigating its potential for fatigue crack length estimation are scarce. Crack growth information enables prediction of the remaining useful life of a component using well established fracture mechanics principles. Hence, the prospects of AE for use in structural health monitoring applications would be significantly improved if it could be demonstrated not only as a means of detecting crack growth but also for estimation of crack lengths. A new method for deducing crack length has been developed based on correlations between AE signals generated during fatigue crack growth and corresponding cyclic loads. A model for crack length calculation was derived empirically using AE data generated during fatigue crack growth tests in 2 mm thick SEN aluminium 2014 T6 specimens subject to a tensile stress range of 52 MPa and an R ratio of 0.1. The model was validated using AE data generated independently in separate tests performed with a stress range of 27 MPa. The results showed that predictions of crack lengths over a range of 10 mm to 80 mm can be obtained with the mean of the normalised absolute errors ranging between 0.28 and 0.4. Predictions were also made using existing AE feature-based methods and the results compared to those obtained with the novel approach developed.

Effects of loading and sample geometry on acoustic emission generation during fatigue crack growth: Implications for structural health monitoring

“…Gagar et al [25] showed that for a stress ratio of 0.1, flow stress of 465.5 MPa and maximum stress values of 58 MPa and 30 MPa, crack closure will occur at cyclic stresses less than 51% and 53% of the respective maximum stress values. However there are no AE signals generated between crack lengths of 25 mm and about 100 mm at stresses between 30% and 95% of the cyclic maximum.…”

Toward identifying crack-length-related resonances in acoustic emission waveforms for structural health monitoring applications

“…[1][2][3][4] The acoustic emission (AE) technique of NDE/ SHM has been used for structural defects detection for many years. [5][6][7][8][9][10][11][12] It can be categorized in the passive detection of the structural defects since it uses the defect as the passive source of AE. Recently, the fatiguecrack-related AE detection has attracted much attention to the researchers.…”

A robust signal processing method for quantitative high-cycle fatigue crack monitoring using soft elastomeric capacitor sensors

“…Nevertheless, human operations are needed as opposed to automatic and continuous fatigue crack monitoring. Other advanced technologies like acoustic emission [6] and piezoelectric sensor [7] also show potentials for crack detection; but as tradeoffs, they require complex test setup and the accuracy of the detection result may depend on the noise level. Computer vision based crack detection algorithms have also been applied to detect certain types of cracks [8] .…”