This paper investigated the co-design problem of less conservative integrated security control and communication for a nonlinear cyber-physical system (CPS) with an actuator fault and false data injection (FDI) attacks. Firstly, considering the efficient utilisation and allocation of computing and communication resources, an integrated framework was proposed from the perspective of active defence against FDI attacks. Secondly, the actuator fault and FDI attacks were augmented as a vector, and a robust observer was proposed to estimate the system state, actuator fault and FDI attacks. Furthermore, based on the obtained estimation results and the location of the FDI attack in the dual-end network, we designed an integrated security control strategy of active attack tolerance and active fault tolerance and, by constructing Lyapunov–Krasovskii functions and using time-delay system theory and the affine Bessel–Legendre inequality, a less conservative co-design method for integrated security control and network communication resource saving was developed. Finally, a simulation experiment of a quadruple tank was carried out to demonstrate the effectiveness of the proposed method.

With the continuous advancement of industrial intelligence, the application of hydraulic robots is becoming increasingly widespread, and the demand for their health diagnosis and maintenance is becoming more urgent. By integrating digital twin (DT) and deep learning technologies, this paper presents an intelligent fault diagnosis method for hydraulic robots based on multi-source information fusion. Firstly, a fault diagnosis architecture and solution for hydraulic robots based on DT technology are proposed. Secondly, a DT model of the hydraulic robot, which incorporates a 3D model and an attribute model with virtual–physical synchronization capabilities, is established, and a calibration method for the twin model is explored. Next, for four typical faults—leakage in the hydraulic system, valve sticking, damping hole blockage, and filter blockage—fault mechanism analysis and evolution process simulation are conducted on the established DT model. A multi-source high-quality dataset, covering normal operating conditions and multiple fault scenarios, is constructed to drive the data twin model. Finally, a feature extraction method combining Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory (BiLSTM), and Attention mechanisms is proposed. This is followed by using a Random Forest (RF) classifier to achieve accurate fault diagnosis for various hydraulic system failures. The experimental results validate the effectiveness and practicality of this method.