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Updated: May 6, 2026

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Método de filtrado de la forma de onda de la emisión acústica basado en el autoencoder y la red neuronal BP

Shunli Jiang1, Kangpei Zheng2, Jinghui Jv3

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Resumen

Este estudio introduce una red neuronal de autocodificación y retropropagación para filtrar las señales de emisión acústica (AE), mejorar la separación del ruido y mejorar la precisión del monitoreo de la salud estructural en estructuras de hormigón.

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Área de la Ciencia:

  • Ingeniería
  • Ciencias de los materiales
  • Procesamiento de señales

Sus antecedentes:

  • La superposición de la señal de emisión acústica (AE) complica el análisis preciso.
  • La reducción efectiva del ruido es crucial para un control fiable de la salud de las estructuras (SHM).

Objetivo del estudio:

  • Desarrollar y evaluar un nuevo método de filtrado de la forma de onda para la separación de la señal AE.
  • Mejorar la precisión de la localización de fuentes de AE en estructuras concretas.

Principales métodos:

  • Utilizó un autocodificador combinado con una red neuronal de retropropagación (BP) para el filtrado de señales.
  • Validación del modelo mediante experimentos de ruptura de plomo y simulaciones numéricas.

Principales resultados:

  • El modelo de red neuronal autoencoder-BP logró una alta precisión de clasificación: 96% para las señales AE y 98% para el ruido.
  • Los datos filtrados mejoraron significativamente la precisión de localización de las fuentes de AE.

Conclusiones:

  • El método de procesamiento de señales AE propuesto separa efectivamente las señales AE del ruido.
  • Esta técnica ofrece un potencial significativo para mejorar el control de la seguridad en estructuras de hormigón.