Scholarlink

<p>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 <i>Echo State Networks</i> (ESN), a simplified version of <i>Recurrent Neural Networks</i> (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 offline 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 differences, while still observing the generated faults. Residual colour maps are used for the visualisation of anomalies.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Adaptive model based on ESN for anomaly detection in industrial systems

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年1月25日

查看全文
  • José Ramón Rodríguez-Ossorio,
  • Antonio Morán,
  • Juan J. Fuertes,
  • Miguel A. Prada,
  • Ignacio Díaz,
  • Manuel Domínguez

摘要

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 simplified 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 offline 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 differences, while still observing the generated faults. Residual colour maps are used for the visualisation of anomalies.