As engineering systems grow increasingly complex, traditional fault detection methods struggle to maintain high accuracy and reliability. This chapter introduces a novel fault diagnosis framework for hydraulic systems, leveraging DT technology. The framework combines a virtual model, built with Modelica, with real-time system data through an innovative bidirectional data consistency evaluation mechanism. To further enhance the data’s reliability, a two-dimensional signal warping algorithm is applied. The optimized data is then used to train a multi-channel one-dimensional convolutional neural network-gated recurrent unit model, which captures both spatial and temporal features, improving fault detection accuracy. The method is validated using a subsea blowout preventer in laboratory settings, achieving an accuracy rate of 95.62%. This approach outperforms existing methods, offering a robust and scalable solution for predictive maintenance in complex hydraulic systems by integrating DT technology, data optimization, and advanced deep learning techniques.