Wei Li

This study addresses an integrated security control and communication co-design problem for nonlinear CPSs subject to coexisting FDI attacks and actuator faults. A novel adaptive discrete event-triggered communication scheme (ADETCS) is proposed. Its triggering threshold adapts to the system state. State estimation, fault estimation, and attack detection are all migrated to the control unit. Based on this framework, a closed-loop T–S fuzzy model is established for active defense against actuator faults and dual-end FDI attacks. A robust augmented observer is then developed via Lyapunov stability theory to jointly estimate system states, actuator faults, and FDI attacks. Sufficient conditions are further derived for an integrated security controller that unifies attack tolerance and fault tolerance. Simulation results on a quadruple-tank system show that the proposed method effectively counteracts coexisting attacks and faults while significantly reducing resource consumption. Over an 800-s horizon, data transmissions drop to 712 (8.9% transmission rate). The sensor-node computational load is also reduced from 8000 time-triggered executions to 712 event-triggered ones.

Performance degradation of actuators in complex multi-axis synchronization systems seriously threatens system reliability and operational safety. Traditional time-based maintenance cannot handle nonlinear degradation of multiple components, often resulting in synchronization failures. This paper presents a new predictive maintenance (PdM) framework combining statistical state estimation and reinforcement learning. First, the Kalman filter, EM algorithm, and RTS smoother are used to accurately estimate actuator health states from noisy measurements. Then, a Mahalanobis distance indicator weighted by the entropy weight method is applied to fuse multi-dimensional data into a unified Health Degree (HD). Finally, a [ ]-learning agent is designed to optimize maintenance decisions, balancing synchronization accuracy and maintenance cost. Simulation results based on industrial stage machinery show that the proposed method effectively extends the remaining useful life (RUL) and ensures high synchronization reliability under various degradation conditions.

For the incipient faults of vehicle power supply under imbalanced datasets, the traditional shallow network has the problems of limited feature extraction ability and the insufficient generalization ability of a single network model. In this paper, an AdaBoost-SAE deep ensemble diagnosis method, which combines the Stacked Auto-Encoder (SAE) deep network and Adaptive Boosting (AdaBoost) algorithm, is proposed. First, SAE is used as a weak classifier to learn and extract incipient fault features from the monitoring date of vehicle power supply. Secondly, in the iterative training process of the model, the classification performance of a single SAE is improved step-by-step by constantly adjusting the weights of the misclassified samples in the training set. Finally, the multiple weak classifiers are combined into strong classifiers by linear weighting to achieve accurate identification of incipient faults under imbalanced datasets. The test results demonstrate that the proposed method can mine deeper features of incipient faults and effectively improve the adverse effects of sample imbalance. Compared with traditional fault diagnosis models and a single SAE, the accuracy of the incipient fault diagnosis can reach 96.6%. Furthermore, the F1-scores of the various working conditions also increased significantly.

As the scale of industrial systems continues to expand, the workload involved with maintaining sensor health has surged. This has led to an increasingly urgent need for autonomous diagnostic capabilities in sensor measurement systems. However, the absence of reference values presents a major challenge in achieving sensor self-diagnosis under unsupervised conditions. Therefore, this study proposes a sensor self-diagnosis design method based on the integration of Binary Convolutional Autoencoder and Bayesian Inference (BCAE-BI). First, to extract the redundancy relationships among sensor measurement data, a binary convolutional autoencoder (BCAE) was used to encode the measurement data features into binary form. Second, using the encoded binary data, fault statistical models for sensors were established for both cases with and without reference values, considering both the statistical independence and dependence of sensor measurement data. Under conditions without reference values, a Gaussian Copula is used to construct a multivariate statistical dependency model to reduce the parameter scale, while the maximum mutual information coefficient is applied to evaluate dependencies between parameters. Bayesian inference (BI) is then utilized to achieve fault self-diagnosis and quantify the uncertainty of diagnostic results. Finally, using the nickel flash smelting system as an example, experiments were conducted for the self-diagnosis models in both modes. The experimental results demonstrate the feasibility and effectiveness of the proposed method in both scenarios.

With the wide application of quadrotor unmanned aerial vehicles (UAVs), the requirements for their safety and reliability are becoming increasingly stringent. In this paper, based on the feedback of airframe performance health perception information and the predictive function control strategy, the autonomous maintenance of a quadrotor UAV with multi-actuator degradation is realised. Autonomous maintenance architecture is constructed by the predictive maintenance (PdM) idea and the Laguerre function model predictive pontrol (LF-MPC) strategy. Using the two-stage Kalman filter (TSKF) method, based on the established UAV degradation model, the aircraft state and actuator degradation state are predicted simultaneously. For the predictive perception of system health, on the one hand, the system health degree (HD) based on Mahalanobis distance is defined by the degree of airframe state deviation from the expected state, and then the failure threshold of the UAV is obtained. On the other hand, according to the degradation state of each actuator, a comprehensive degradation variable fused with different weight coefficients of multiple actuators degradation is used to obtain the probability density function (PDF) of remaining useful life (RUL) prediction. For the autonomous maintenance of system health, the LF-MPC weight matrixes are adjusted adaptively in real-time based on the HD evaluation, to achieve a compromise balance between UAV performance and control effect, and greatly extend the working time of UAV. Simulation results verified the effectiveness of the proposed method.

This research studies the co-designing security control and adaptive event-triggered communication for a nonlinear CPS that experiences DoS attacks and actuator faults. First, a new communication scheme that adjusts adaptively to events is developed, and the threshold for triggering this scheme dynamically varies based on the state of the system, resulting in a more efficient utilization of network communication resources. Additionally, utilizing ADETCS as a foundation, a structure for safeguarding nonlinear CPS is constructed. Thirdly, the assistance of Lyapunov theory, time delay system theory and less conservativeness techniques are utilized, a observer is designed to observe the estimation of state and fault in real time, along with a security controller to ensure tolerance against attacks and faults. Finally, the simulation case of the classical quadruple-tank model is adopt to verify the viability and efficiency of the proposed approach.

The coordinated design of comprehensive security control and communication is investigated for an industrial cyber-physical system (ICPS) that incorporates stealthy false data injection (FDI) and actuator faults, using data-driven and mechanism analysis methods. Firstly, replacing the conventional discrete event-triggered communication scheme with an adaptive discrete event-triggered communication scheme (ADETCS), a resilient ICPS framework is developed to combat network FDI attacks and actuator faults. Secondly, based on data-driven technology, the ELM prediction model of the FDI attack is established to reconstruct and compensate for the attack accurately. Then, by employing the affine Bessel-Legendre inequality and the augmented Lyapunov-Krasovskii (L-K) functional, the analytical solutions for the robust observer and integrated safety controller are derived. Lastly, the effectiveness of the proposed approach is validated through a quadruple-tank example.

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.

Based on the discrete event-triggered communication scheme (DETCS), a codesign method of dual security control and communication for the non-linear cyberphysical system (CPS) with denial-of-service (DoS) attack and actuator fault is studied under multi-objective constraints. First, to effectively distinguish DoS attacks with different energy levels, the DoS attack detection method is established based on the maximum allowable delay of the system. Second, a non-linear CPS dual security control framework that integrates active-passive attack tolerance for DoS attack and active fault tolerance for actuator fault is constructed. Third, the low-energy DoS attack is regarded as a special kind of time delay, and a robust observer for the estimation of system states and actuator fault is designed, and a codesign method of passive attack tolerance, active fault tolerance, and communication is proposed. Furthermore, a correction compensation algorithm of control quantity is proposed for high-energy DoS attack based on the PID idea to make active-passive attack tolerance more effective. Finally, the effectiveness of the proposed method is verified by the simulation of a quadruple-tank model.