Industrial AI in condition-based maintenance: A case study in wooden piece manufacturing
Otros/as autores/as
Fecha de publicación
2024ISSN
0360-8352
Resumen
The article presents a case study applying industrial artificial intelligence to Condition-Based Maintenance
in a wooden piece manufacturing company. The study focuses on the extraction system that transports
wood residue to a warehouse, supplying a biomass plant for cold and heat generation in the factory. The
objective is to predict the temperature of the ten induction motors in the extraction system using an Extreme
Learning Machines-based methodology, enabling dynamic model prediction. Data from IoT sensors measuring
the motors’ intensity, temperature, and humidity are collected every minute, pre-processed, and stored in a
database. The pre-processing includes a single novel algorithm to detect and eliminate data containing possible
sensor blockages. The results demonstrate an implementable methodology utilizing single-layer feedforward
neural networks, prioritizing fast training while maintaining sufficient accuracy for detecting deviations in
motor behaviour. The research offers valuable insights for preventive maintenance applications in similar
industrial settings.
Tipo de documento
Artículo
Versión del documento
Versión publicada
Lengua
Inglés
Materias (CDU)
62 - Ingeniería. Tecnología
Palabras clave
Páginas
18 p.
Publicado por
Elsevier
Citación
Martí Puig, P., Amar Touhami, I., Colomer Perarnau, R., Serra Serra, M. (2024) Industrial AI in condition-based maintenance: A case study in wooden piece manufacturing. Computers and Industrial Engineering, 188, num: 109907. https://doi.org/10.1016/j.cie.2024.109907
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