Hydraulic robots are increasingly used in industrial systems, which raises the need for reliable health diagnosis and maintenance under complex operating conditions. An intelligent fault diagnosis method was developed by integrating digital twin technology with deep learning and combining a Transformer, a long short-term memory networks (LSTM), and extreme gradient boosting (XGBoost). A diagnosis architecture based on digital twins was built, and cyber-physical synchronization was made possible by a digital twin with an attribute model and a three-dimensional model. Calibration improved twin accuracy and virtual-physical consistency. Fault mechanism analysis and fault evolution simulation were conducted for four typical hydraulic system faults: leakage, valve sticking, damping orifice blockage, and filter blockage. The simulations generated a dataset covering normal and fault conditions for data-driven modeling and diagnosis. A Transformer-LSTM feature extractor captured global dependencies and temporal dynamics in multi-dimensional time-series data, and XGBoost performed multi-fault classification. The experimental verification results show that the proposed method demonstrate steady performance under various noise disturbances and 96.6% diagnostic accuracy across many problems, suggesting high resilience and generalization and supporting hydraulic robot fault detection and maintenance in industrial applications.

Data-driven technology and model mechanism analysis techniques are combined to study the problem of multi-modal integrated safety control and communication collaborative design, with an eye toward a type of industrial cyber-physical system (ICPS) that is susceptible to dual-end asynchronous denial of service (DoS) attacks and actuator failures. Firstly, an adaptive discrete event triggered communication scheme (ADETCS) with a trigger threshold that can dynamically change with the system behavior is designed, and an ICPS multi-modal integrated safety control architecture that can simultaneously resist asynchronous dual-end DoS attacks and actuator failures is constructed. Secondly, an active-passive collaborative hybrid intrusion tolerance strategy based on data-model linkage is proposed. Then, combined with the idea of “divide and conquer”, a tolerance method for dual-end asynchronous DoS attacks is proposed with the help of a long short-term memory (LSTM) networks and elastic control. Thirdly, the observer and controller are deduced based on Lyapunov stability theory, and then the K-Means++ clustering algorithm and fuzzy fusion method are used to perform weighted fusion of controllers under different modes online, thus realizing soft switching between different control modes. Finally, the effectiveness of the proposed method is verified through a four-tank example. According to the experimental results, the data-model linkage method improves ICPS's resistance to dual-end asynchronous DoS attacks, and the multi-modal integrated safety controller's design enables two-way adaptive cooperative control between the ADETCS and control mode.

With the development of industrial technology, the health diagnosis and maintenance of centrifugal pumps are increasingly urgent. Combining digital twin and machine vision technology, this paper proposed an intelligent impeller fault diagnosis method for centrifugal pumps based on a digital twin flow field cloud diagram. First of all, the digital twin model of the centrifugal pump was used to simulate the evolution of the random fracture for the impeller blades, and the pressure and velocity cloud diagrams of the impeller flow field with different fault characteristics were generated. Secondly, based on the learning and training of the Yolov5 algorithm, two kinds of machine vision models, namely pressure and velocity cloud diagrams, were obtained, and the preliminary diagnosis of impeller fault was realized by combining statistical analysis. Furthermore, the complementary advantages of the two types of detection models were considered, and the two types of detection models were combined based on the idea of stack integration to improve the accuracy of impeller fault diagnosis. The experimental verification shows that the intelligent fault diagnosis method for centrifugal pumps proposed in this paper has a diagnosis accuracy of more than 0.99 for the random fracture of impeller blades. The developed intelligent impeller fault diagnosis system for centrifugal pumps makes the method developed in this paper be applied to practical scenarios.