This paper presents a data-driven framework for integrating encryption transmission and attack detection in cyber-physical systems (CPS) with nonlinear physical plants. The main focus of this research is to use deep neural networks to realize the coprime factorization (CF) of nonlinear systems. The definition of the CF guides the network training and designing process, and the model’s topology is designed in the state-space form, which improves the interpretability of the data-driven CF. Based on the CF-aided neural networks, an encrypted transmission module is designed that projects information related to system dynamics into a perpendicular data space, which complements existing encryption methods from a control theory perspective. Subsequently, an anomaly detector are designed using the same CF pairs. This detector not only provides high-accuracy detection of attacks but also distinguishes between attacks and faults, thereby reducing the false positive rate and enhancing the reliability of the attack detection. The proposed method has been validated in a real CPS using a mecanum-wheeled vehicle as the physical plant, demonstrating its effectiveness and applicability.

Aiming at the current challenges in converting electrical parameters to indicator diagrams, a subspace-aided indicator diagram estimation approach is proposed to establish a data-driven mapping model from electrical to force parameters, which helps avoid the need for analyzing the mechanism model of tower-type pumping systems. Specifically, the lifting technique is adopted based on the subspace method to construct the space of the electrical parameter signals, addressing the correspondence between input and output signals, while preventing the loss of effective information. Then, a recursive indicator diagram estimation approach is proposed, utilizing the updating/downdating of the Cholesky decomposition to enable online updating of the data-driven mapping model. In addition, for tower-type pumping systems operating under multiple conditions, a gap metric indicator is developed as a test statistic to determine the switching of operating conditions. The effectiveness of the proposed methods is verified through experimental measurements from tower-type pumping systems in actual oil wells.

This article proposes a projection-aided robust distributed monitoring and control optimization approach for interconnected systems with disturbances. The disturbances and state coupling between subsystems are a challenge in achieving accurate distributed process monitoring using data-driven techniques. To address the problems, a distributed adaptive residual generator uses the average consensus algorithm to perform data fusion on the subsystem residual generator to implement disturbance decoupling process monitoring. The key to implementing this process is to use input and output data disturbance in the perturbed orthogonal complementary space to drive the adaptive residual generator. Then, using the projection technique, the residual signal in the disturbance space drives the distributed learning of plug-and-play (PnP) controller parameters. The average consensus algorithm ensures that the subsystem PnP controller parameter gradient consistency converges to the centralized design. The feasibility and effectiveness of the proposed approach are verified and demonstrated through a simulation.

This article focuses on utilizing process data to detect faults in nonlinear systems. To accomplish this, stable image/kernel representation is learned for nonlinear systems using deep neural networks, which serve as the basis for residual generators and fault detection. First, the closed-loop image representation of nonlinear systems is identified using gate recurrent units and fully connected neural networks. The involved network topology is designed to learn the nonlinear mapping in the form of linear time-varying state space, allowing the extension of existing linear methods to nonlinear systems. Then, with the identified image representation, the data-driven realization of kernel representation is derived. Finally, the residual generator is developed utilizing the system's kernel representation to enable precise fault detection in nonlinear systems. The effectiveness of our study is demonstrated through a numerical benchmark study and an actual experiment on a real Mecanum-wheeled vehicle platform.

With the booming development of fields such as cloud computing, big data, and artificial intelligence, the productivity of data have experienced explosive growth, prompting the expansion of edge computing. However, as data-intensive applications continue to increase, memory systems based solely on Dynamic Random Access Memory (DRAM) are no longer able to meet the demands of edge computing for high memory footprints, low access latency, and low energy consumption. In this paper, we organize DRAM as a cache for Non-Volatile Memory (NVM) and propose a utility-based hybrid memory data placement (UDP-HM) strategy, to address energy efficiency issues. UDP-HM places data in a hybrid memory system by calculating page utility. A series of simulation experiments are conducted to verify and evaluate the proposed hybrid memory data placement strategy. Under different workload intensities, compared to UH-MEM and RBLA, UDP-HM shows an average increase of 8.71% and 9.21% respectively in DRAM cache read/write operations, an average increase of 10.18% and 13.94% respectively in latency, an average decrease of 35.24% and 37.18% respectively in energy consumption, and an average decrease of 26.48% and 31.68% respectively in energy-delay product.

Aiming at the problem of colored noise in the signal, this article proposes a subspace frequency estimation approach under colored noise with application to fault diagnosis of motor rolling bearings. First, a nonlinear discrete-time system is described to generate colored noise. An extended I/O model with parameters of a nonlinear discrete-time system is given by the subspace method. Then, the gap metric-aided system order determination approach is developed for extended observability matrix identification. Then, the data-driven diagnostic observer parameter identification approach and the fast approximate power iterative subspace method are adopted to realize online monitoring for frequency change detection. Eventually, a data-driven design scheme of residual generator is proposed for the implementation of fault detection. The effectiveness of the proposed methods is verified for fault diagnosis performance through numerical simulations and the experimental measurements from the dynamic motor rolling bearing experiment rig.

In this study, a two-stage method which extracts fuzzy rules directly from samples is proposed for classification. First, we introduce a neighborhood based attribute significance algorithm to select r of the most important attributes from the original attribute set. Second, the proposed algorithm generates fuzzy rule from each sample described by the selected attribute subset and finally simplifies the returned fuzzy rule-base. A confidence degree is assigned for each of the extracted fuzzy rules by counting the number of training samples covered by the rule to solve the conflicts among the rules and then the rule-base is pruned. The performance of the proposed classification method have been compared with other five classification approaches including C4.5, DTable, OneR, NNge, and PART on seven UCI data sets. The experimental results show that the proposed method is better than other methods in two aspects: the higher classification accuracy and the smaller rule-base.