Gemelos digitales y mantenimiento predictivo en industrias manufactureras de Huancayo

• José Manuel Armada Pacheco https://orcid.org/0000-0003-3827-6144
• Dulio Oseda Gago https://orcid.org/0000-0002-3136-6094
• David Ramos Piñas https://orcid.org/0000-0002-4215-2374
• Gilvonio Yaranga Franklin Moises https://orcid.org/0000-0003-1804-0169

Palabras clave: Gemelos digitales, mantenimiento industrial, modelos de machine learning, análisis predictivo, infraestructuras industriales

Resumen

La presente investigación se propuso evaluar el impacto de la implementación de gemelos digitales integrados con modelos de machine learning predictivo en la optimización del mantenimiento industrial de empresas textiles en la ciudad de Huancayo, donde se generan fallos mecánicos que implican hasta 12 horas aproximadamente de paro o inactividad. Se orientó bajo una metodología mixta, con un diseño pre-post intervención con 72 expertos a través de encuestas (72) y entrevistas (15) a personas que tienen roles de técnicos, gerentes y supervisores en industrias textileras en Huancayo, Perú. Para el análisis se apoyó básicamente en softwares tecnológicos como SPSS (ANOVA) para efectos de lo cuantitativo y el ATLAS.TI para el análisis cualitativo, además del apoyo del Power BI para sustentar de mejor forma los hallazgos encontrados en la categorización. Los resultados apuntan a que hay reducciones significativas en los tiempos de respuesta a fallos, mejoras en usabilidad (SUS) y aumentos en MTBF (p<0.01), evidenciando un impacto positivo del gemelo digital. Se concluye que los gemelos digitales con ML predictivo pueden facilitar la transición de mantenimiento correctivo a predictivo en contextos textiles andinos como los de Huancayo.

Citas

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