As the complexity of modern engineering systems increases, traditional fault detection models face growing challenges in achieving accuracy and reliability. This paper presents a novel Digital Twin-assisted fault diagnosis framework specifically designed for hydraulic systems. The framework utilizes a virtual model, constructed using Modelica, which is integrated with real-time system data through a first-of-its-kind bidirectional data consistency evaluation mechanism. The integrated data is further refined using a two-dimensional signal warping algorithm to enhance its reliability. This optimized twin data is then employed to train a multi-channel one-dimensional convolutional neural network-gated recurrent unit model, effectively capturing both spatial and temporal features to improve fault detection. The subsea blowout preventer in lab is used to study the performance of the method. The results show that the accuracy is 95.62 %. Compared to current methods, this is a significant improvement. By integrating DT technology, data consistency optimization, and advanced deep learning techniques, this framework provides a scalable and reliable solution for predictive maintenance in complex engineering systems.

中文翻译：

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一种用于液压系统的数字孪生辅助智能故障诊断方法


随着现代工程系统复杂性的增加，传统的故障检测模型在实现准确性和可靠性方面面临着越来越大的挑战。本文提出了一种专为液压系统设计的新型数字孪生辅助故障诊断框架。该框架利用使用 Modelica 构建的虚拟模型，该模型通过首创的双向数据一致性评估机制与实时系统数据集成。使用二维信号翘曲算法进一步细化集成数据，以提高其可靠性。然后，利用这种优化的孪生数据来训练多通道一维卷积神经网络门控循环单元模型，有效地捕获空间和时间特征，以提高故障检测能力。实验室中的海底防喷器用于研究该方法的性能。结果表明，准确率为 95.62 %。与目前的方法相比，这是一个显著的改进。通过集成 DT 技术、数据一致性优化和高级深度学习技术，该框架为复杂工程系统中的预测性维护提供了可扩展且可靠的解决方案。