Detection of Uniaxial Fatigue Stress under Magnetic Flux Leakage Signals using Morlet Wavelet

Firdaus, Syed Muhamad; Arifin, Ahmad Mubarak Tajul; Abdullah, Shahrum; Singh, Salvinder Singh Karam

doi:10.3221/igf-esis.61.17

Cited by 3 publications

(4 citation statements)

References 25 publications

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“…According to ASM International, the range of strain rates used for tensile tests should be 10 −5 s −1 to 10 −2 s −1 . High strain rates ranging from 10 −1 s −1 to 10 1 s −1 can cause changes in the temperature and behaviour of a material [ 28 ].…”

Section: Methodsmentioning

confidence: 99%

“…Each specimen was subjected to uniaxial fatigue testing at a frequency of 10 Hz and with loads ranging from 50% to 85% of the Ultimate Tensile Strength (UTS) of API steel grade X65, which was obtained from the tensile testing, to ascertain how load variations affected the magnetic signals. The selection of the frequency value was based on the frequency used in previous studies that used the metal magnetic memory method during fatigue testing of metal materials [ 28 , 32 , 33 ]. In addition, Arifin et al found that the fatigue test was unstable when using a frequency of 20 Hz during testing [ 34 ].…”

Section: Methodsmentioning

confidence: 99%

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Fatigue Life Assessment of API Steel Grade X65 Pipeline Using a Modified Basquin Parameter of the Magnetic Flux Leakage Signal

et al. 2023

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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.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Fatigue Life Assessment of API Steel Grade X65 Pipeline Using a Modified Basquin Parameter of the Magnetic Flux Leakage Signal

et al. 2023

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“…W f (a,b) is the wavelet transform coefficient. In this paper, complex Morlet wavelet [35] was chosen as the base wavelet function, and the frequency corresponding to the maximum wavelet variance was taken as the main frequency of each IMF.…”

Section: Noise Reduction In Life Signalmentioning

confidence: 99%

A Multi-Target Localization and Vital Sign Detection Method Using Ultra-Wide Band Radar

Zhang

Cheng

et al. 2023

Sensors

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Life detection technology using ultra-wideband (UWB) radar is a non-contact, active detection technology, which can be used to search for survivors in disaster rescues. The existing multi-target detection method based on UWB radar echo signals has low accuracy and has difficulty extracting breathing and heartbeat information at the same time. Therefore, this paper proposes a new multi-target localization and vital sign detection method using ultra-wide band radar. A target recognition and localization method based on permutation entropy (PE) and K means++ clustering is proposed to determine the number and position of targets in the environment. An adaptive denoising method for vital sign extraction based on ensemble empirical mode decomposition (EEMD) and wavelet analysis (WA) is proposed to reconstruct the breathing and heartbeat signals of human targets. A heartbeat frequency extraction method based on particle swarm optimization (PSO) and stochastic resonance (SR) is proposed to detect the heartbeat frequency of human targets. Experimental results show that the PE—K means++ method can successfully recognize and locate multiple human targets in the environment, and its average relative error is 1.83%. Using the EEMD–WA method can effectively filter the clutter signal, and the average relative error of the reconstructed respiratory signal frequency is 4.27%. The average relative error of heartbeat frequency detected by the PSO–SR method was 6.23%. The multi-target localization and vital sign detection method proposed in this paper can effectively recognize all human targets in the multi-target scene and provide their accurate location and vital signs information. This provides a theoretical basis for the technical system of emergency rescue and technical support for post-disaster rescue.

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“…They obtained high operability and low error rates within the acceptable threshold of an online scanning pipeline detector. Firdaus et al (2022)…”

Section: Introductionmentioning

confidence: 99%

Defect detection in pipelines using magnetic flux leakage method combined with Kernel Extreme Learning Machine

Li,

Sun

2024

Preprint

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Magnetic flux leakage (MFL) testing technology is widely employed in non-destructive testing of pipelines, and the analysis of leakage signals plays a crucial role in assessing safety. This paper introduces a novel approach for MFL testing, which combines finite element simulation with artificial neural networks. Firstly, a simulation model is developed to study MFL testing in defective pipelines, with a focus on investigating how magnetization state and defect dimensions impact the leakage signal. Signal features are subsequently defined and extracted through an analysis of the distribution curve of MFL signals. Finally, a Kernel Extreme Learning Machine (KELM) model is constructed, and optimization is carried out on the influential parameters to predict defect dimensions. The results demonstrate that the effect of magnetization intensity on the magnetization state can be categorized into two stages: a nonlinear growth stage and a saturated linear growth stage. Additionally, an increase in defect size has the effect of delaying the magnetization process in pipelines. Notably, changes in defect dimensions have a pronounced impact on the distribution of MFL signals. And KELM prediction model is demonstrated to be capable of accurately and efficiently predicting the depth, length, and area of defects.

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Detection of Uniaxial Fatigue Stress under Magnetic Flux Leakage Signals using Morlet Wavelet

Cited by 3 publications

References 25 publications

Fatigue Life Assessment of API Steel Grade X65 Pipeline Using a Modified Basquin Parameter of the Magnetic Flux Leakage Signal

Fatigue Life Assessment of API Steel Grade X65 Pipeline Using a Modified Basquin Parameter of the Magnetic Flux Leakage Signal

A Multi-Target Localization and Vital Sign Detection Method Using Ultra-Wide Band Radar

Defect detection in pipelines using magnetic flux leakage method combined with Kernel Extreme Learning Machine

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