Multiple parameters fusion of electromagnetic nondestructive inspection data for evaluation of fatigue damage in steel elements

Abstract

The use of electromagnetic nondestructive testing methods to assess damage and fatigue changes is a subject of numerous studies. However, the observed signals are influenced by many factors. Thus, making a proper assessment on the basis of single parameters’ behavior is complicated task. Therefore, in this paper, an approach of multiple parameters fusion for evaluation of damage was presented. The range of material changes in steel sample after successive stages of fatigue process was monitored using Magnetic Barkhausen Noise and AC magnetization method in a selected two-dimensional region. The acquired data was then processed for the need of signals parametrization. The procedures in time, frequency and time-frequency domains were applied. Then the selection of representative features was carried out before the information was combined via multivariate regression. Finally, the obtained function was used to achieve the two-dimensional maps of evaluated damage for successive fatigue periods.

Keywords

Fatigue evaluation nondestructive evaluation magnetic Barkhausen noise AC magnetization method multi-source data fusion

Get full access to this article

View all access options for this article.

References

Sorsa

Leiviskä

Santa-aho

and Lepistö

, Quantitative prediction of residual stress and hardness in case-hardened steel based on the Barkhausen noise measurements, NDT&E Int.46 (2012), 100–106.

Sagar

S.P.

Parida

Das

Dobmann

and Bhattacharya

D.K.

, Magnetic Barkhausen emission to evaluate fatigue damage in low carbon structural steel, Int. Journal of Fatigue27 (2005), 317–322.

Maleque

M.A.

and Salit

M.S.

, Materials Selection and Design: Mechanical Failure of Metals, Springer (2013), 17–38.

Dobmann

, Probability of detection – the approach to combine probabilistic fracture mechanics with NDT – where we are? Badania Nieniszcza̧ce i Diagnostyka1–2 (2016), 27–33.

Palma

E.S.

Mansur

T.R.

Ferreira Silva

Jr and Alvarenga

Jr, Fatigue damage assessment in ANSI 8620 steel using Barkhausen noise, Int. Journal of Fatigue27 (2005), 659–665.

Psuj

, Fusion of Multiple Parameters of Magnetic Testing Results for Damage Assessment of Loaded Steel Structures, Studies in Applied Electromagnetics and Mechanics40 (2015), 192–199.

Soultan

et al., Mechanical Barkhausen noise during fatigue of iron, NDT&E Int.39 (2006), 493–498.

C.C.H.

Tang

Jiles

D.C.

and Biner

S.B.

, Evaluation of fatigue damage using a magnetic measurement technique, IEEE Trans. on Magn.35 (1999), 3977–3979.

Lindgren

and Lepist

, Effect of cyclic deformation on Barkhausen noise in a mild steel, NDT&E Int.36 (2003), 401–409.

10.

Dion

Kumar

and Ramuhalli

, Multi-Sensor Data Fusion for High-Resolution Material Characterization, in: AIP Conf. Proc., Rev. of Quantitative Nondestructive Evaluation, Vol. 894, 2007, pp. 1189–119.

11.

Padovese

L.R.

Martin

and Millioz

, Time-frequency and time-scale analysis of Barkhausen noise signals, Proc. of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering223 (2009), 577–588.

12.

Miesowicz

Staszewski

W.J.

and Korbiel

, Analysis of Barkhausen noise wavelet-based fractal signal processing for fatigue crack detection, Int. Journal of Fatigue83 (2016), 109–116.

13.

Mirai

Iliana

and Yoshifuru

, Frequency fluctuation signal processing and its application to the Barkhausen signals, Int. Journal of Applied Electromagnetics and Mechanics52 (2016), 371–379.

14.

Roffo

Melzi

and Cristani

, Infinite Feature Selection, in: IEEE Int. Conf. on Computer Vision (ICCV), Chile, 2015.