A digital twin-assisted intelligent fault diagnosis method for hydraulic systems

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.