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Classifying of welding time series based on multi-scale time irreversibility analysis and extreme learning machine

Author

Listed:
  • Huang, Yong
  • Yang, Dongqing
  • Wang, Lei
  • Wang, Kehong

Abstract

In welding, previous research has proven that the behavior of its arc droplet is chaotic and fractal by the complex analysis of time series. In this study, the multi-scale time irreversibility (MSTI) method is used to analyze welding time series, and then used to classify the weld quality. By comparing the results of shuffled time series with original time series, it is confirmed that the time irreversibility is an intrinsic property of welding time series and it can extend over multiple scales. The superposition effect of multiple frequency components of welding time series results in enhances time irreversibility. The MSTI index (Pm%, Gm%) is used to quantify the time irreversibility, and has a capable of distinguishing different series. In the finally, the index (Pm%, Gm%) is combined with extreme learning machine (ELM) to classify the weld quality, and obtain good classification results.

Suggested Citation

  • Huang, Yong & Yang, Dongqing & Wang, Lei & Wang, Kehong, 2020. "Classifying of welding time series based on multi-scale time irreversibility analysis and extreme learning machine," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    • Handle: RePEc:eee:chsofr:v:139:y:2020:i:c:s0960077920304380
    DOI: 10.1016/j.chaos.2020.110040
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    References listed on IDEAS

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