In this article, for the nonzero-sum (NZS) differential games problem of uncertain nonlinear systems with state constraints, an adaptive robust stabilization scheme based on the control barrier function (CBF) is presented under the influence of random disturbances and control input matrix uncertainty. To deal with the impact of uncertainty on the system, the nominal system of the original system is adopted and the cost functions associated with each player are appropriately chosen to convert the robust regulation problem of multiplayer differential games into an optimal regulation problem. Furthermore, the purpose of combining the cost function relevant to each player with the CBF is to make the system states evolve in the safe area. Different from the classical actor–critic dual neural network (NN), each player only needs a critic NN to approach the corresponding cost function without the restriction of the initial stabilizing control. Combined with the Lyapunov stability theory, under the combined influence of random disturbances and state constraints, the state and critic NN weights of the closed-loop system are guaranteed to be uniformly ultimately bounded (UUB). Finally, two simulation examples are used to verify the validity of the presented scheme.

Satellite networks have many advantages over traditional terrestrial networks. However, it is very difficult to design a satellite network with excellent performance. The paper briefly summarizes some existing satellite network routing technologies from the perspective of both single-layer and multilayer satellite constellations, and focuses on the main ideas, characteristics, and existing problems of these routing technologies. For single-layer satellite networks, two routing strategies are discussed, virtual node strategy and virtual topology strategy. Moreover, considering the deficiency of existing multilayer satellite network routing, we discuss the topic invulnerability. Finally, the challenges and problems faced by the satellite network are analyzed and the trend of future development is predicted.

The whole process of the petrochemical industry involves flammable and explosive dangerous goods. The timely discovery of abnormalities or failures in the petrochemical process is crucial to ensure production safety. This paper sets up the approach to build the Digital Twin System (DTs) of a petrochemical process. Specifically, we decompose the petrochemical process into five levels one by one and build a digital twin plug-in for each component of the component layers, and then inversely decouple the process to assemble the DTs layer by layer. As a specific experimental example, the characteristic DTs is proposed to build modules of temperature field and pressure field and flow field, these DT modules are driven by practical industrial sampling data from cracking furnace, and three characteristic DTS modules stated above are integrated to form DTS. Based on the digital twin technology and DTs, we propose the logical structure of chemical process status monitoring and fault diagnosis in detail, which improves the safety and controllability of the petrochemical process.

Purpose: Abnormal changes in temperature directly affect the stability and reliability of a gyroscope. Predicting the temperature and detecting the abnormal change is great value for timely understanding of the working state of the gyroscope. Considering that the actual collected gyroscope shell temperature data have strong non-linearity and are accompanied by random noise pollution, the prediction accuracy and convergence speed of the traditional method need to be improved. The purpose of this paper is to use a predictive model with strong nonlinear mapping ability to predict the temperature of the gyroscope to improve the prediction accuracy and detect the abnormal change. Design/methodology/approach: In this paper, an double hidden layer long-short term memory (LSTM) is presented to predict temperature data for the gyroscope (including single point and period prediction), and the evaluation index of the prediction effect is also proposed, and the prediction effects of shell temperature data are compared by BP network, support vector machine (SVM) and LSTM network. Using the estimated value detects the abnormal change of the gyroscope. Findings: By combined simulation calculation with the gyroscope measured data, the effect of different network hyperparameters on shell temperature prediction of the gyroscope is analyzed, and the LSTM network can be used to predict the temperature (time series data). By comparing the performance indicators of different prediction methods, the accuracy of the shell temperature estimation by LSTM is better, which can meet the requirements of abnormal change detection. Quick and accurate diagnosis of different types of gyroscope faults (steps and drifts) can be achieved by setting reasonable data window lengths and thresholds. Practical implications: The LSTM model is a deep neural network model with multiple non-linear mapping levels, and can abstract the input signal layer by layer and extract features to discover deeper underlying laws. The improved method has been used to solve the problem of strong non-linearity and random noise pollution in time series, and the estimated value can detect the abnormal change of the gyroscope. Originality/value: In this paper, based on the LSTM network, an double hidden layer LSTM is presented to predict temperature data for the gyroscope (including single point and period prediction), and validate the effectiveness and feasibility of the algorithm by using shell temperature measurement data. The prediction effects of shell temperature data are compared by BP network, SVM and LSTM network. The LSTM network has the best prediction effect, and is used to predict the temperature of the gyroscope to improve the prediction accuracy and detect the abnormal change.

Operational safety and reliability have become increasingly paramount in large and complex rotating machinery in industrial manufacturing. Recent strides in data-driven fault diagnosis have significantly advanced the fields of machinery health assessment, fault identification, and prediction. This review delves into the existing literature on fault diagnosis technology for rotating machinery, categorizing the various diagnostic approaches. It further examines the evolution of fault diagnosis technology based on dimensionless indices and elucidates the five latest progressions of foundational theories: the design and optimization of dimensionless indices, extraction and features of mutual dimensionless indices, fault diagnosis technology based on dimensionless indices and artificial immune system, fusion technology of dimensionless indices in time and frequency domains, and datasets. The applications of fault diagnosis technology based on dimensionless indices in the petrochemical industry, high-speed railway, shipping and aerospace are discussed. Future directions for research are outlined across six key areas: more excellent dimensionless indices, rapid and efficient detectors, fault analysis for small-sample fault events, theoretical systems of time–frequency domain fusion diagnosis, fault diagnosis with standardized test database, and expert knowledge base. This comprehensive review bridges the research gap by systematically examining the roles and applications of dimensionless indices in the fault diagnosis of rotating machinery.