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Feature extraction and neural network-based fatigue damage detection and classification

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Abstract

This paper proposes a methodology for detection and classification of fatigue damage in mechanical structures in the framework of neural networks (NN). The proposed methodology has been tested and validated with polycrystalline-alloy (AL7075-T6) specimens on a laboratory-scale experimental apparatus. Signal processing tools (e.g., discrete wavelet transform and Hilbert transform) have been applied on time series of ultrasonic test signals to extract features that are derived from: (i) Signal envelope, (ii) Low-frequency and high-frequency signal spectra, and (iii) Signal energy. The performance of the neural network, combined with each one of these features, is compared with the ground truth, generated from the original ultrasonic test signals and microscope images. The results show that the NN model, combined with the signal-energy feature, yields the best performance and that it is capable of detecting and classifying the fatigue damage with (up to) 98.5% accuracy.

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Notes

  1. Manufacturer: MTS®, Berlin, New Jersey, USA.

  2. Manufacturer: OLYMPUS®, Shinjuku, Tokyo, Japan.

  3. Manufacturer: QUESTAR®, New Hope, Pennsylvania, USA.

  4. The reason for using five (apparently) identical specimens is to build both consistency and credibility of experimental results. The rationale is that, due to the uncertainties accruing from internal defects and the machining process, the fatigue life of similar test specimens may significantly duffer.

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Acknowledgements

The first author gratefully acknowledges the financial support of the Saudi Arabian Cultural Mission (SACM). The work reported in this paper has been supported in part by U.S. Air Force Office of Scientific Research (AFOSR) under Grant No. FA9550-15-1-0400.

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Authors and Affiliations

  1. Department of Mechanical Engineering, Taibah University, Medina, KSA, 42353, Saudi Arabia

    Hassan Alqahtani

  2. Department of Mechanical Engineering, Pennsylvania State University, University Park, PA, 16802, USA

    Asok Ray

  3. Department of Mathematics, Pennsylvania State University, University Park, PA, 16802, USA

    Asok Ray

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  1. Hassan Alqahtani
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  2. Asok Ray
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Correspondence to Hassan Alqahtani.

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Alqahtani, H., Ray, A. Feature extraction and neural network-based fatigue damage detection and classification. Neural Comput & Applic 34, 21253–21273 (2022). https://doi.org/10.1007/s00521-022-07609-3

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