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<p>The possibilities of using artificial neural networks and deep learning algorithms for automating the processing of acoustic emission (AE) signals to identify fracture stages are considered using the analysis of the latest studies. The accuracy of the results for different approaches is compared, and their advantages and disadvantages are highlighted. Deep learning methods have significant potential for practical implementation in AE diagnostics.</p>

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Methods of artificial intelligence for acoustic emission diagnostics of fracture stages (a review). Part 2: artificial neural networks and deep learning

  • 期刊论文

  • 发表日期：

    2025年1月1日

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  • O. M. Stankevych,
  • D. P. Rebot

摘要

The possibilities of using artificial neural networks and deep learning algorithms for automating the processing of acoustic emission (AE) signals to identify fracture stages are considered using the analysis of the latest studies. The accuracy of the results for different approaches is compared, and their advantages and disadvantages are highlighted. Deep learning methods have significant potential for practical implementation in AE diagnostics.