In this paper, a distributed adaptive output feedback consensus tracking control scheme is developed for heterogeneous nonlinear multi-agent systems (MASs) with actuator failures. Unlike most existing results, the parameters in the considered MAS are unknown, and the system's nonlinear functions are not required to satisfy the Lipschitz condition. A kind of K-filters with an additional design parameter is designed to suppress the effects of unknown actuator failures. In addition, an event-triggering mechanism with a switching threshold method is designed to reduce the communication burden. With the proposed control scheme, the tracking error of each subsystem converges exponentially to an adjustable bound. Finally, a nonlinear MAS consisting of multiple single-link robot manipulators is given to verify its effectiveness.

Accelerated degradation testing (ADT) data typically exhibit a time-stress-dependent structure, as well as random uncertainties due to time-varying effects and unit-to-unit variations. Existing ADT models based on Brownian motion with drift have successfully represented the fault/failure-based degradation behavior and random uncertainty by assuming that the drift parameter follows a Gaussian distribution. However, these models often lack robustness to outliers, leading to distorted analysis, affecting parameter estimation, model accuracy, decision-making, risk assessment, and potentially overlooking the influence of stress factors. A novel robust ADT model based on the Wiener process and its corresponding lifetime analysis method are proposed to address these issues. The proposed approach improves upon traditional ADT models by making the drift parameter follow a $t$ -distribution rather than a Gaussian distribution, which can reduce sensitivity to outliers in real degradation processes. In addition, the proposed method allows for the simultaneous consideration of time-stress-dependent factors in the ADT model, facilitating the derivation of a closed-form robust ADT formulation. Subsequently, the lifetime is analyzed based on the ADT model using the first hitting time method in a probabilistic framework. The proposed method is applied to stress relaxation data of electrical connectors and compared to three other common methods.

This study investigates the distributed fault-tolerant consensus issue of multi-agent systems subject to complicated abrupt and incipient time-varying actuator faults in physical hierarchy and aperiodic denial-of-service (DoS) attacks in networked hierarchy. Decentralized estimators are devised to estimate consecutive system states and actuator faults. A unified framework with an absolute local output-based closed-loop estimator in decentralized fault estimation design and a relative broadcasting state-based open-loop estimator in distributed event-triggered fault-tolerant consensus design is developed. Criteria of exponential consensus of the faulty multi-agent systems under DoS attacks are derived by virtue of average dwelling time and attack frequency technique. Simulations are outlined to confirm the efficacy of the proposed distributed fault-tolerant consensus control algorithm based on an event-triggered mechanism.

Abstract Traditional manual testing methods are no longer able to meet the maintenance needs of modern shipboard fire control system board-level circuits. Therefore, the development of intelligent and universal automatic test systems has become an important issue for shipboard fire control system fault detection. This article analyzes the performance and functional requirements of the automatic test system, develops the automatic test system based on PXI bus instruments, and designs an overall solution with signal time-frequency analysis capabilities that can carry out intelligent fault diagnosis for a variety of board-level circuits. On this basis, the design of hardware circuit and software solution is completed. The automatic test system was analyzed on hardware indicators and software functions. On the hardware side, the hardware resource selection was completed based on the hardware indicator requirements. On the software side, the overall software architecture design and the design of each functional module were completed based on the system software functional requirements. Finally, the composition and functions of each module of the system software and the overall operation process are introduced.

Fault detection is crucial for saving valuable maintenance time and costs of industrial systems when faults occur. Faults are usually cataloged into sudden faults and degraded faults, which have diverse impacts on system safety. So, distinct treatment should be treated during fault detection. In this paper, an intelligent fault detection method is proposed based on a double-convolutional neural network (CNN) model architecture to detect whether faults occur and which type they belong. Firstly, a CNN model is employed to judge whether faults occur. Furthermore, the "Majority rule" is applied to effectively eliminate the influence of outliers for enhancing the robustness of fault detection model. Then, Another CNN model combined with root-mean-square (RMS) is introduced to detect fault types that belong to the sudden fault or degraded fault. Finally, an experimental study involving four types of bearing degradation scenarios is conducted to validate the effectiveness of the proposed method.

Testability is a crucial issue in the field of fault detection and diagnosis (FDD). Testing every parameter of a system is an unacceptable option in practice, because it often generates an overwhelming amount of data with sparse value density and costs a lot of time and resource consumption. Optimal test selection design is essential for effectively performing FDD strategies. This paper focuses on the unreliable test problem in testability to minimize the test cost subjected to lower constraints on fault detection, and fault isolation. Expectation-maximization and Bayesian network are applied to solve the missed detection issue. To avoid the local optimum phenomenon, a combination of simulated annealing algorithm and genetic algorithm method is adopted to search the optimal test sequence. The proposed optimal test selection design scheme under unreliable tests is implemented on the Automatic Gauge Control (AGC) simulation platform in Baosteel Co., Ltd., Shanghai, China. The results show that the obtained optimal test sequence can greatly reduce the test cost, and ensure good fault detection and isolation performance.

Lithium-ion batteries are a crucial component of new energy. Studying the fault diagnosis method of the batteries can ensure the safety of the system during operation. In this paper, a diagnosis method is studied based on the efficient channel attention (ECA) mechanism. The ECA is combined with convolutional neural network (CNN) and long short-term memory (LSTM) to design the diagnosis model. To determine the values of hyperparameters, a hyperparameter selection method based on genetic-firefly algorithm is implemented. The hyperparameter optimization methods are illustrated by the simulated battery fault data. The results show that the proposed method can make the classification accuracy reach 100%.

The rotor of the magnetic suspension turbomachinery is supported by the magnetic suspension bearing without contact and mechanical friction, which directly drives the high-efficiency fluid impeller. It has the advantages of high efficiency, low noise, less fault and no lubrication. However, the system often has some unknown mutation, time variation, load perturbation and other un-certainties when working, and the traditional Proportion Integration Differentiation (PID) control strategy has great limitations to overcome the above disturbances. Therefore, this paper firstly establishes a mathematical model of the rotor of magnetic levitation turbomachinery. Then, a linear active disturbance rejection controller (LADRC) is presented, which can not only improve the above problems of PID control, but also avoid the complex parameter tuning process of traditional nonlinear active disturbance rejection control (ADRC). However, LADRC is easy to induce the overshoot of the system and cannot filter the given signal. On this basis, an improved LADRC with a fast-tracking differentiator (FTD) is proposed to arrange the transition process of input signals. The simulation results show that compared with the traditional PID controller and single LADRC, the improved linear active disturbance rejection control method with fast tracking differentiator (FTD-LADRC) can better suppress some unknown abrupt changes, time variation and other uncertainties of the electromagnetic bearing-rotor system. At the same time, the overshoot of the system is smaller, and the parameters are easy to be set, which is convenient for engineering application.

In this paper, the problem of distributed adaptive consensus tracking control for second-order nonlinear heterogeneous multi-agent systems (MASs) with input quantization is considered. A distributed output feedback control scheme based on a K-filter is developed to suppress the influences of unknown disturbances and input quantization. In contrast to existing approaches, an additional design parameter is introduced into the controller design to ensure that the subsystem tracking error converges to an arbitrarily small residual set. Through Lyapunov stability analysis, it can be proved that the proposed control scheme can achieve distributed consensus tracking control of second-order nonlinear heterogeneous MASs. In addition, all signals in the closed-loop system are shown to be globally uniformly bounded. Finally, a practical example demonstrates the effectiveness of the proposed control method.

In this paper, the distributed formation tracking control problem of quadrotor unmanned aerial vehicles is considered. Adaptive backstepping inherently accommodates model uncertainties and external disturbances, making it a robust choice for the dynamic and unpredictable environments in which unmanned aerial vehicles operate. This paper designs a formation flight control scheme for quadrotor unmanned aerial vehicles based on adaptive backstepping technology. The proposed control scheme is divided into two parts. For the position subsystem, a distributed robust formation tracking control scheme is developed to achieve formation flight of quadrotor unmanned aerial vehicles and track the desired flight trajectory. For the attitude subsystem, an adaptive disturbance rejection control scheme is proposed to achieve attitude stabilization during unmanned aerial vehicle flight under uncertain disturbances. Compared to existing results, the novelty of this paper lies in presenting a disturbance rejection flight control scheme for actual quadrotor unmanned aerial vehicle formations, without the need to know the model parameters of each unmanned aerial vehicle. Finally, a quadrotor unmanned aerial vehicle swarm system is used to verify the effectiveness of the proposed control scheme.

With widespread use in areas such as electric vehicles and portable electronic devices, lithium-ion batteries are favored for their high energy density and long cycle life. However, accurate prediction of battery life remains a challenging issue and is critical to the effective management and use of these energy storage devices. Battery degradation phenomena can lead to performance degradation, which in turn negatively impacts battery applications. Traditional research methods are often limited by time and resource constraints, making it difficult to conduct long-term life prediction studies. To overcome these problems, this paper proposes an optimization method based on Particle Swarm Optimization and Simulated Annealing algorithms. The method aims to search for the optimal parameter combinations by the SA algorithm and further finetune the parameters by the PSO algorithm to optimize the Least Squares Support Vector Machine (LS-SVM) model and achieve more accurate battery life prediction. The experimental results, based on real battery aging data sets, show that the proposed hybrid optimization method outperforms the traditional optimization method and the independent LS-SVM model in terms of accuracy and robustness. The method can be applied to a variety of Li-ion battery systems and provides a valuable tool for battery management and decision making. Future work will further validate the proposed method on a larger dataset and integrate it with a real-time battery monitoring system for online lifetime prediction.

Optimal sensor allocation can substantially reduce the life cycle maintenance costs of engineering systems. Considerable effort has been exerted to model the causal relationship between sensors and faults, but without considering the propagation of fault risk. In this paper, a grey relational analysis (GRA) based quantitative causal diagram (QCD) sensor allocation strategy is proposed that can take account of the influence of the propagation of fault risk. QCD is used to describe both the fault-sensor causal relationship and the fault-to-fault causal relationship. A data-driven-based GRA is applied in QCD to calculate the coefficients of the propagation of fault risk. To achieve an accurate relationship between faults and sensors, an improved quantitative analytic hierarchy process is proposed to calculate the coefficients between faults and sensors that is defined as sensor detectability in this paper. An optimal sensor allocation strategy is then developed using an improved particle swarm optimization (IPSO) algorithm under the constraint on sensor detectability to minimize fault unobservability and total cost. The proposed strategy is demonstrated by a case study on a single-phase inverter system. Compared with two other sensor allocation strategies, the results show that the proposed strategy can obtain the lowest fault unobservability of per unit cost (−0.242) for sensor allocation under the propagation of fault risk.

Speed control in inland water systems needs to achieve effective balance between ship operational efficiency and transport safety. However, speed limit regulations are largely formulated through expert judgment rather than objective evidence-based evaluation, which sometimes leads to inefficiency due to subjective bias. In this study, a new method is proposed to evaluate the performance of shipping traffic under current speed limits by using the automatic identification system (AIS) big data of 4923 ships in the Shanghai section of the Yangtze River in China. The key elements of this method include data acquisition, error elimination, combination of ship AIS and waterway geocoded data to model traffic flow characteristics, and estimation of the correlation between ship speed and congestion level. Shipping traffic performance in different segments is analyzed. Results reveal that the overall compliance to the speed limit is high, and only a few over-speeding cases are noted in certain segments. Furthermore, we use a normal distribution to model the correlation between ship speed and traffic volume. The findings indicate that the current speed limit in the Shanghai section of Yangtze River is rational. This work provides useful insights into testing the rationality of speed limits in other waterways or shipping channels.

为了研究双吸泵所受径向力,采用RNGk-ε湍流模型分别对原型双吸泵和重心偏移后的双吸泵进行数值模拟.通过模拟分别得出了原型双吸泵和不同偏心距和偏心角度下双吸泵外特性、中心截面以及作用在叶轮上的径向力特征,将其进行互相对比分析.研究结果表明:设计工况下,4种偏心距和4种偏心角度的扬程上下浮动范围在0％～0.5％,效率降低范围在0.08％～1.18％,小流量工况和设计工况下,偏心距越大,径向力变化越明显,越接近设计工况时,叶轮所受径向力越小;特别地,0.8Qd,ω=180°时,Fd=2>Fd=6>Fd=8,在ω=180°,d=4 mm时径向力出现较大范围波动.径向力较为理想的几点为:0.8Qd下,ω=0°、1.0Qd下,ω=270°和1.2Qd下,ω=0°和ω=270°;通过对比设计工况下原型泵和偏心泵试验结果,表明本文所用数值计算方法及三维模型可靠性较高,进行径向力分析具有一定可信度..

The test sequencing problem is optimized to reduce test cost and ensure the performance of Fault detection and diagnosis (FDD), which is the intersection of FDD and Design for Testability (DFT). Considering the false and missing alarm of complex system diagnosis, this paper proposed an optimal test sequence strategy for fault diagnosis under unreliable test based on the quasi-depth first search (QDFS) algorithm. Firstly, the method established a heuristic evaluation function considering fault diagnosis abilities, information entropy, test cost and the reliability of the test. Then, the quasi-depth first search algorithm is used to build the fault diagnosis strategy. The theory and experiments demonstrate that the cost based on the QDFS method is much better than greed algorithm. Therefore, the proposed method could be used to design the strategy for fault diagnosis under unreliable test.

The sterile insect technique (SIT) is an effective weapon to prevent transmission of mosquito-borne diseases, in which sterile mosquitoes are released to reduce or eradicate the wild mosquito population. To study the impact of the sterile insect technique on the disease transmission, we formulate stage-structured discrete-time models for the interactive dynamics of the wild and sterile mosquitoes using Beverton-Holt type of survivability, based on difference equations. We incorporate different strategies for releasing sterile mosquitoes, and investigate the model dynamics. Numerical simulations are also provided to demonstrate dynamical features of the models.

基于贝叶斯理论,论文提出了一种关于油井信息价值的评价方法.通过对评价井信息可靠程度的评价,对钻前地质认识的先验概率进行修正,并形成后验概率,基于先验概率和修正概率,计算钻前与钻后项目的最大期望净现值及两者的差值.该方法在巴西某深水油田的应用结果表明,基于对某油田评价井信息可靠程度的认识,钻后某油田的高、中地质储量的发生概率均有所增加,而低地质储量的发生概率有所降低;基于该修正概率下的开发方案优化可以对项目的经济性产生约2百万美元的正效益,即该评价井所录取信息的价值为2百万美元,进一步验证了信息价值方法在量化决策方面的可行性.

深水油田勘探开发存在经验不足,深水环境及地下因素复杂多变、单井投资费用高企,以及项目成本回收压力大等挑战,简单复制陆上和浅海的勘探开发一体化模式已无法满足深水油田勘探开发的需要.为此引入地质工程一体化理念,综合考虑地下不确定性、时间要素和成本要素,提出一套适合深水油田的油藏评价及决策体系,并在此基础上开展评价模式、决策方法以及钻完井配套技术研究.在巴西盐下深水油田的评价实践表明,以分区评价为核心的“一体双轴”的评价模式和以信息价值为代表的深水油田评价井决策方法,将有利地推动深水油田的勘探开发一体化进程,缩短评价周期,进而实现油藏评价的效益最大化和评价决策的科学化.盐下钻完井配套技术的应用,大幅提高了钻井效率,在为开发井积累经验的同时,降低了钻井成本.地质工程一体化理念在巴西利布拉油田的应用,证明了地质工程一体化作业模式在深水油田以及油藏评价阶段的适应性,从而扩大了地质工程一体化的应用领域.

With an attempt to study the influence of the discharge plate in a horizontal tube,the mathematic model for the flow and heat transfer of the horizontal tube was established using the coupled Level-Set and Volume of Fluid method.The simulation results are in agreement with the experimental data.The results show that in the case of small flow discharge plate can stable the liquid film an d alleviate "dry spot" phenomenon due to intermittent drip.The liquid film distribution of horizontal tube with drainage plate is more uniform,which can affect the temperature distribution of liquid film.Horizontal tube with drainage plate can restrain the collision and mixing at the bottom of tube.Increasing flow rate lead to the formation of stagnation zone of the liquid film and the deterioration of heat transfer.Adding liquid discharging board can speed up the liquid film flow rate at the bottom of horizontal tube.