This paper investigates the high stress concentration zone for fatigue crack estimation on the damaged tower crane pulley by measuring magnetic flux leakage. Fatigue crack analysis is important to prevent any component failure that could harm workers. Precautions must be taken to locate the possibilities of failure region on a tower crane pulley. For this study, magnetic flux leakage signals were measured using metal magnetic memory method to predict the high stress concentration area. The respective signals were collected for 30 scanning lines on the tower crane pulley surface and merged to provide a better graphical view to estimate the irregularities or defect location more accurately. The metal magnetic memory polar mapping was compared with the finite element analysis results for validation. The highest von Mises stress value obtained through the finite element analysis is 167.84 MPa using three-dimensional tetrahedral mesh for the size of 3 mm. Thus, the comparison revealed the similarities of stress concentration zone between the results and will produce more accurate prediction for the actual irregularity or defect location on the failed component.

This paper demonstrates the application of continuous wavelet transform technique for magnetic flux leakage signal generated during a uniaxial fatigue test. This is a consideration as the magnetic signal is weak and susceptible to being influenced by an external magnetic field. The magnetic flux leakage signal response of API steel grade X65 is determined using Metal Magnetic Memory under cyclic load conditions ranging from 50% to 85% of the UTS. To facilitate further signal analysis, the magnetic flux gradient, the dH(y)/dx signal were converted from a length base into time series in this study. Magnetic flux leakage readings indicated a maximum UTS load of 56.5 (A/m)/mm at 85%, where a higher load resulted in a higher reading and the signal contained Morlet wavelet coefficient energy of 1.02×106 µe2/Hz. As increasing percentages of UTS loads were applied, the signal analysis revealed an increasing linear trend in the dH(y)/dx and wavelet coefficient energy. The analysis revealed a strong correlation between the wavelet coefficient energy and the dH(y)/dx amplitude, as indicated by the coefficient of determination (R2) value of 0.8572. Hence, this technique can provide critical information about magnetic flux leakage signals that can be used to detect high stress concentration zones.

This paper presents a modified fatigue life model of the Basquin equation using the stress parameter of the magnetic flux leakage signal. Most pipeline steels experience cyclic loading during service and the influence of the load history makes assessing fatigue behaviour more difficult. The magnetic flux leakage signal’s response to a uniaxial cyclic test of API X65 steel was measured with eight levels of ultimate tensile stress loads. The influence of dH(y)/dx on fatigue failure was the main concern in this study, the aim being to represent localised stress parameters in the modified Basquin equation. Both fatigue lives, experimental and predicted from the modified Basquin equation, were validated through reliability analysis, producing a 60% value when approaching 1.8 × 105 cycles. The fatigue data from the experiment produced a higher mean-cycle-to-failure value than the prediction data, with slightly different values of 3.37 × 105 and 3.28 × 105. Additionally, the modified Basquin equation’s predicted and the experimental fatigue lives were found to have a high R2 correlation value of 0.9022. The Pearson correlation also showed a good relationship between the fatigue lives, with an r value of 0.9801. Finally, the modified Basquin equation based on dH(y)/dx signals provided an accurate and alternative method for durability assessment.

PurposeA tower crane mainly ensures the success or efficiency of building construction. Fatigue crack analysis is important for tower crane components to prevent any accidents to workers in construction sites caused by component failure and to ease the maintenance or replacement of failed components. This work aimed to characterise the damage of failed components, analyse the relationship between the metal magnetic memory (MMM) result and the damage of failed components, and to validate the relationship between MMM and finite element analysis (FEA).Design/methodology/approachMMM was used in this work to detect any irregularities or early failure on the basis of the high stress concentration zone of ferromagnetic steel using magnetic flux leakage. Magnetic flux leakage was used on the MMM device to achieve the first objective using the MMM system by detecting the irregularities. The results of MMM analysis were validated through comparison with FEA results by determining their relationship.FindingsMMM results show that the position of defects on the tower crane pulley is within the stress area shown on FEA.Originality/valueHence, MMM method is a potential tool in monitoring failure mechanism in construction site.

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