Adaptive model based on ESN for anomaly detection in industrial systems
[EN]Modeling real industrial systems is a challenging task due to the complex nature of process data. Data-driven models commonly employ machine learning algorithms, but they often lack the ability to adapt to changes in the system over time. This paper proposes a method that uses Echo State Network...
| Autores: | , , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Universidad de León |
| Repositorio: | BULERIA. Repositorio Institucional de la Universidad de León |
| OAI Identifier: | oai:buleria.unileon.es:10612/23990 |
| Acceso en línea: | https://hdl.handle.net/10612/23990 |
| Access Level: | acceso abierto |
| Palabra clave: | Ingeniería de sistemas Echo state networks · Dynamics Data-based modelling Visual information · Online learning 3306 Ingeniería y Tecnología Eléctricas |
| Sumario: | [EN]Modeling real industrial systems is a challenging task due to the complex nature of process data. Data-driven models commonly employ machine learning algorithms, but they often lack the ability to adapt to changes in the system over time. This paper proposes a method that uses Echo State Networks (ESN), a simplifed version of Recurrent Neural Networks (RNN), to model an industrial plant. The ESN model incorporates online adaptation to system changes and enables the visualisation of deviations or errors in the operation of the plant. This adaptive model acknowledges acceptable changes within the original system while identifying potential problems or errors. The advantage of this approach is that the same model can be applied to other systems with the same design, eliminating the need for algorithm retraining. Firstly, its successful ofine application in visualising anomalies applied to the reference plant is assessed. Secondly, the model is tested for online adaptation to changes in another plant with an identical design but slight diferences, while still observing the generated faults. Residual colour maps are used for the visualisation of anomalies. |
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