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Un método novedoso de diagnóstico de fallos entre dominios para procesos industriales multicon dición basado en

Xin Qin¹, Xuan Guo¹, Jie Dong¹

¹Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, 100083, PR China.

Neural networks : the official journal of the International Neural Network Society

|January 13, 2026

Resumen

Un novedoso método de adaptación de meta-dominio mejora el diagnóstico de fallos en procesos industriales complejos. Este enfoque mejora la precisión en diversas condiciones de trabajo mediante la alineación de características y el uso de meta-aprendizaje progresivo para una mejor generalización.

Sus antecedentes:

Los procesos industriales complejos exhiben alta dinámica y acoplamiento inter-sistemas, lo que afecta la eficiencia y la calidad del producto.
La fabricación personalizada introduce variabilidad, lo que desafía los métodos tradicionales de diagnóstico de fallos.
La adaptación de dominio es crucial para un diagnóstico de fallos multicon dición efectivo.

Conclusiones:

El método de adaptación de meta-dominio mejora significativamente el diagnóstico de fallos en condiciones industriales variables.
El enfoque ofrece una solución robusta para la detección de fallos industriales en el mundo real.
Este trabajo avanza la aplicación del aprendizaje automático en la monitorización de procesos industriales.

Palabras clave:

redes neuronales adversarias diagnóstico de fallos entre dominios adaptación de meta-dominio meta-aprendizaje progresivo

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