Wavelet-Based Feature Extraction Algorithm for Fatigue Strain Data Associated with the k-Means Clustering Technique

Teuku Edisah Putra; Shahrum Abdullah; Dieter Schramm; Mohd Zaki Nuawi; Tobias Bruckmann

doi:10.4028/www.scientific.net/AMR.891-892.1717

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Plastic Slip and Early Stage of Fatigue Damage in Polycrystals: Comparison between Experiments and Crystal Plasticity Finite Elements Simulations
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Wavelet-Based Feature Extraction Algorithm for Fatigue Strain Data Associated with the k-Means Clustering Technique
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Fatigue Behavior and Surface Sensitivity of Board-Shaped Sample of Powder Metallurgy FGH 96
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Prediction of Fatigue Crack Growth of Aged Hardening Al-Alloys under Variable Amplitude Loading
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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 891-892Wavelet-Based Feature Extraction Algorithm for...

Wavelet-Based Feature Extraction Algorithm for Fatigue Strain Data Associated with the k-Means Clustering Technique

Abstract:

The study presents the development of a wavelet-based segmentation algorithm for fatigue life assessment. Strain data was extracted using the Morlet family. The extraction process identified damaging segments, and it was able to shorten the original signal by 74.3%, with less than 10% difference with statistical parameters. The extraction algorithm was able to retain at least 97.9% of fatigue damage. The damaging segments drawn were clustered using the k-means method to provide three groups of segments, i.e., lower, moderate, and higher groups representing statistical values. The approach was suggested as an alternative method for evaluating and clustering fatigue strain signals.

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Periodical:

Advanced Materials Research (Volumes 891-892)

Pages:

1717-1722

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.891-892.1717

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Online since:

March 2014

Authors:

Teuku Edisah Putra, Shahrum Abdullah, Dieter Schramm, Mohd Zaki Nuawi, Tobias Bruckmann

Keywords:

Data Clustering, Fatigue Data Editing, K-Means, Statistics, Wavelet

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