Stochastic process models can capture the stochastic dynamics, and they have been widely applied in component degradation modeling. However, the model uncertainty may lead to inaccurate and unreliable prediction of the remaining useful life (RUL). In addition, time-varying working conditions and maintenance should be incorporated into the degradation model to accurately determine the component degradation level. To solve the aforementioned problem, a generalized degradation model, based on the nonlinear Tweedie exponential dispersion process (TEDP) considering random effects, covariates, and imperfect maintenance, is developed. Firstly, the nonlinear TEDP model with random parameters is proposed to construct the component degradation model. Considering the time-varying operating environment, the effect of the continuous and discrete covariates on the component degradation is modeled based on the proportional hazards model. The effect of the imperfect maintenance is also incorporated into the component degradation model. Then, mean-field variational inference, stochastic gradient variational inference, and Expectation-Maximum (EM) algorithms are used together to estimate the unknown parameters in order to improve the mathematical interpretability of the developed models. Finally, simulation studies and two real cases on GaAs laser and hydraulic pump are used to demonstrate the effectiveness and validity of the proposed model and algorithm.

The winch’s performance under complex sea conditions is significantly influenced by its collecting and releasing processes. To enhance its performance and reliability, an optimization approach considering wave disturbances and control laws is proposed to balance time efficiency and tension stability. Within a multiobjective optimization framework, the method designs constant tension control and robust adaptive speed control and introduces sinusoidal acceleration trajectories to minimize tension surges and reduce system impacts caused by rapid starts/stops. The constant tension controller reduces wave disturbances, while the speed controller manages the working process. These controllers are designed with unknown reference signals determined during the optimization process. Additionally, the objective functions in the optimization phase aim to reduce working time and tension fluctuations, with constraints ensuring system safety and mission requirements. Furthermore, an experimental platform constructed on a ship validates the accuracy of the winch model. The optimized process not only shortens operational time, as collecting same length only consumption 127.44 s compared 143.14 s without optimization, but also reduces tension and acceleration. More importantly, transitions between states become more gradual. This indicates that the proposed method is both time‐efficient and effective in dampening tension fluctuations and mitigating the effects of abrupt changes during the working process.

Cyber-physical systems (CPSs) facilitate the integration of physical entities and cyber infrastructures through the utilization of pervasive computational resources and communication units, leading to improved efficiency, automation, and practical viability in both academia and industry. Due to its openness and distributed characteristics, a critical issue prevalent in CPSs is to guarantee resilience in the presence of node injection attack. This survey provides a detailed and comprehensive overview of recent advances on resilient coordination against node injection attacks for CPSs based on the idea ofmean-subsequence-reduction(MSR). Different from existing survey papers that adopt a horizontal taxonomy based on attack type, we propose a vertical taxonomy according to the multi-layered framework of CPS, which enables a more in-depth of the latest developments in MSR-based resilient coordination results. Furthermore, the proposed taxonomy fills the gap that no survey has gathered and classified numerous MSR-type papers from the perspective of system structure. Specifically, more than one hundred MSR-based resilient coordination results are classified and reviewed from three perspectives: physical structure, network topology, and communication mechanism. Finally, three typical application scenarios, i.e., resilient frequency synchronization for single-phase microgrids, resilient containment for multi-robot systems, and resilient distributed optimization for multi-microgrid systems are examined, respectively, all of which demonstrate the applicability of the reviewed MSR-based strategies.

Due to its inherent characteristics, the synthetic aperture radar (SAR) image is mainly corrupted by speckle noise, posing additional challenges to lossy image compression algorithms. Traditional optical image compression techniques lack the ability to distinguish between image details and noise, which increases storage costs and restores images that still contain noise. Inspired by these observations, we optimize image compression algorithms to incorporate denoising capabilities, enabling joint denoising and compression of SAR images. Specifically, we transform the raw speckled images into noise-free bitstreams, allowing the subsequent decompression to produce clean images. To achieve this objective efficiently, we introduce a novel knowledge distillation strategy that incurs no additional computational cost. Furthermore, this distillation mechanism yields statistically significant performance improvements across various image compression algorithms. Experimental results demonstrate that when evaluated on both synthetic and real-world datasets, the proposed method not only achieves the best visual effects but also outperforms existing methods in terms of rate-distortion performance, equivalent number of looks, and other quantitative indicators.

Hierarchical frameworks-a special class of directed frameworks with a layer-by-layer architecture-can be an effective mechanism to coordinate robot swarms. Their effectiveness was recently demonstrated by the mergeable nervous systems paradigm (Mathews et al., 2017), in which a robot swarm can switch dynamically between distributed and centralized control depending on the task, using self-organized hierarchical frameworks. New theoretical foundations are required to use this paradigm for formation control of large swarms. In particular, the systematic and mathematically analyzable organization and reorganization of hierarchical frameworks in a robot swarm is still an open problem. Although methods for framework construction and formation maintenance via rigidity theory exist in the literature, they do not address cases of hierarchy in a robot swarm. In this article, we extend bearing rigidity to directed topologies and extend the Henneberg constructions to generate self-organized hierarchical frameworks with bearing rigidity. We investigate three-key self-reconfiguration problems: 1) framework merging; 2) robot departure; and 3) framework splitting. We also derive the mathematical conditions of these problems and then develop algorithms that preserve rigidity and hierarchy using only local information. Our approach can be used for formation control generally, as in principle it can be coupled with any control law that makes use of bearing rigidity. To demonstrate and validate our proposed hierarchical frameworks and methods, we apply them to four scenarios of reactive formation control using an example control law.

Deep learning has achieved success in optical image super-resolution (SR). However, few methods have been proposed for synthetic aperture radar (SAR) images. This is because SAR images inherently suffer from severe speckle noise, and their resolution is typically much lower compared with optical images, making SR more challenging. To simultaneously denoise and preserve texture details in SAR image SR tasks, we propose a joint despeckling and SR network via knowledge distillation (DSRKD). By implementing feature distillation (FD) in the encoding stage, we enable the student network to obtain noise-free latent variables, thereby restoring clean SR images, and simultaneously enhancing effective supervision for the student using target distillation (TD). This distillation strategy does not incur additional computational costs during inference. Experimental results demonstrate that our algorithm effectively suppresses speckle noise while preserving texture details of images on both synthetic and SAR datasets. Compared with other state-of-the-art algorithms, the proposed method achieves superior performance on both visual results and quantitative metrics, such as PSNR, SSIM, and other nonreference indicators under various SR scales and speckle intensities.

This Innovative Practice Full Paper presents a novel educational approach to improve the quality of graduate engineering education by emphasizing real world project based learning, the state-of-the-art and state-of-the-practice technological developments, and a need for globalization. Aiming to address the challenges of educating engineers for the 21stCentury, authors developed a double degree graduate program based on industry-university cooperation. This program includes a multifaceted-industry-university cooperation system, assuring the central role for industry as the crucial component in education of graduate level engineering students. The applied strategies and adopted methods are presented and discussed. After two cohorts of graduate students have successfully completed the program, the outcomes indicate that implemented educational approach has attained desired results. According to the survey results, students benefited significantly from the program and are in high demand by industry. The experience of this industry-university cooperation program can be valuable to other engineering schools and programs.

Accelerated life test (ALT) is commonly used to assess lifetime of mission critical and expensive products. Accurate ALT requires realistic load sequence representative of the actual and/or expected work conditions. A novel method, using the Markov information matrix, is proposed in this paper to produce the load sequence to be applied in the ALT. The proposed method provides several advantages, including improved efficiency as well as capturing dynamic characteristics of the loading conditions, such as transition frequency and cumulative time effect of the load. This paper presents the Markov method for analyzing the load sequence, in which the Markov information matrix is modified by taking into account the cumulative effect of each load level. The model is then applied to the ALT of hydraulic piston pumps, which are widely used in aircraft, in order to perform analysis of the cumulative damage. Considering the actual aircraft operation, hidden Markov model is used to generate the equivalent accelerated load sequence of a pump. The load sequence can be easily applied on a test bench for the verification and approval of pumps. Finally, an accelerated sequence design method is proposed to guide the ALT. The method ensures the consistency of the transfer frequency and the cumulative damage caused by the load, which provides an equivalent sequence for the ALT.

Since failure of mechanical components can lead to catastrophic failure of the entire system, significant efforts have been made to monitor system behavior and try to predict the end of useful life of a component. A method to assess the process of mechanical wear in real time is based on monitoring the amount of debris in the lubricant. Although this approach has shown some potential in application, the nonlinearly cumulative damage in the late stage of mechanical life presents significant challenge to early prediction of the Remaining Useful Life. This paper considers continuous wear (devoid of sudden large particle dislodging or catastrophic failure) and assumes that it is a positive feedback physical process. This assumption serves as a basis of a dynamic model developed to describe the nonlinear behavior of wear in the late stage of useful mechanical life. Based on this model, it was discovered that the inflection point in the cumulative debris, during continuous wear process, presents a more accurate indicator of pending mechanical failure compared to the existing indicators. The peak in generation rate is considered as the end of useful mechanical life. The model is validated based on data from four wind turbine gearboxes. The results indicate that the proposed model and associated indicator can be used with significant confidence to predict the Remaining Useful Life during the early stages of operation and have distinct advantages over the standard linear model (Moving Average Model), both in terms of accuracy and robustness.

Hyperspectral imaging provides new opportunities for improving face recognition accuracy. However, it poses such challenges as difficulty in data acquisition, low signal to noise ratio (SNR), and high dimensionality. In this paper, we propose a novel method for hyperspectral face recognition with good recognition rates. We first reduce noise adaptively from each spectral band and then crop each face according to eye coordinates. We perform minimum noise fraction (MNF) transform to the cropped face data cube in order to extract a number of MNF bands. We perform log-polar transform to each MNF band and extract Fourier spectra from them. In this way, the extracted features are translation-, rotation-, and scale-invariant. We conducted some experiments to test this new method for hyperspectral face recognition with very promising results. For Hong Kong Polytechnic University Hyperspectral Face Database (PolyU-HSPD), we achieved an average correct recognition rate of 92.70% with standard deviation of 2.61 (92.70%±2.61), which is better than 3D Gabor wavelet face recognition (91.30%±2.09), local binary pattern (LBP) classification (90%±2.42), spectral signature-based classification (24.60%±3.87), spectral angle-based classification (25.40%±4.36), and collaborative representation-based classification (CRC) (86.10%±2.37) for this database. When we use all spectral bands in the face dataset, we obtain a correct classification rate of 93.49%±2.53.

Different from the previous mechanical and electrical products, aerospace hydraulic actuation system operates in very short mission time with high reliability requirement. How to design a suitable reliability growth testing for the hydraulic actuation system is urgent to the engineering. This paper designs the reliability growth testing scheme based on Weibull distribution and censored time. Comparing with the traditional reliability growth test based on the Duane model, the reliability growth testing scheme is content to the real application.

High-performance robust synchronization motion control for quad-cylinder electro-hydraulic lift systems with parametric uncertainties and uncertain nonlinearities is considered. Adaptive Fuzzy logic systems are introduced as an approximator to counteract the effects of coupling among acceleration terms. Robust adaptive control by backstepping is used to handle parametric uncertainties and uncertain nonlinearities in quad-cylinder synchronization lift systems. In order to address the problem of “explosion of terms” caused by using backstepping techniques, “dynamic surface control” techniques are introduced. Simulation results on a quad-cylinder synchronization lift system verified the effectiveness of the proposed approach.

The hydraulic power supply system of new generation aircraft requires more safe, reliable, and autonomous, which needs the prognostic and health management (PHM) system. The new prognostic management architecture for aircraft hydraulic power supply system is proposed in this paper according to Open System Architecture for Condition Based Monitoring (OSACBM) standards. In order to obtain failure detective sensors layout, the failure features analysis of aircraft hydraulic power supply system are carried out, and the prognostic management test rig are established. Specific algorithms for different PHM functional layers are employed. Application in the laboratory demonstrates that the proposed PHM architecture for aircraft hydraulic power supply system can realized accurate diagnosis, prognosis and autonomous logistics.

This paper summarized the airworthiness requirements and the A320 aircraft maintenance manual, to propose the top-level design of hydraulic systems based on the airworthiness regulations. On this basis, an all-operation-profile fault reconfiguration strategy of hydraulic system provided to ensure that the system could achieve the optimal fault reconfiguration in different operation profile.

Health monitoring and prognostics of axial piston pump is very helpful for the safety of aerial hydraulic system. Directing to the difficulties of measuring the wear loss between the valve plate and cylinder barrel, this paper presents a hybrid prediction method based on EMD (Empirical Mode Decomposition) and SVM (Support Vector Machine), in which EMD is used to get the pump's health state and PSO (Particle Swarm Optimization)-SVM is used to make a prediction for the pump's remaining useful lifetime. Oil-return flow was selected to indicate the wear condition of pump with several IMFs. SVM was trained by these IMFs, and then be used to predict the oil-return flow one-step ahead or multi-step ahead. In the SVM's training process, PSO was used to search the optimal parameter of SVM's kernel function. Applications show that hybrid prediction method has higher prediction precision and could be applied for the remaining useful lifetime prognostics.

Many high-reliability equipments are always required to be stored for a long time before be used, but the slight changes of performance in high-reliability products during long-term storage are very difficult to be caught and the changes of storage environmental stress and product material properties are also complicated. This paper focuses on the storage reliability for high-reliability products and reviews the researches on storage reliability of the US and Russian weapons and equipments. Then some relative methods are described, such as accumulated damage methods, accelerated storage methods, methods based on the evolution of product storage performance and evaluation methods.

This paper proposed a novel radial maximum temperature thermal network based on hollow cylinder geometry. By local linear fitting method, the nolinear term in the radial maximum-termperature expression of the hollow cylinder was linearized, therefore, the maximum-temperature expression could be described by the thermal network method. A dynamic compensation unit was used in the thermal network in order to eliminate the error introduced by the local linear fitting. Example analysis proved the accuracy and practicality of this novel thermal network.

Two pumps supply oil in parallel in the large aircraft. Due to different frequencies of the pumps, the flow field at T-junction of two oil convergence remains unclear. In this paper, the flow field of T-junction pipe was analyzed at two working status using Shear Stress Transport (SST) model and Finite Volume Method (FVM) in ANSYS/CFX. Our simulation identified the variation law of velocity peak at T-junction and it is found that the eddy and velocity shock exists in the corner of the T-junction. Aiming at this problem, an arc-junction was designed to replace the traditional T-junction. Based on the simulation, the eddy disappeared and the maximum of velocity peak decreased than T-junction. However, the curvature radius of the arc is a key factor which affects the fluid confluence, so 8 series of ρ was tested to get the law of the variation of max velocity. The velocity shock and energy loss are two evaluated index, and through simulation the optimal ρ was got, it can be concluded that the velocity shock decreased when ρincreased and the energy loss reached the minimum value at ρ = 32.52mm.

In this paper, a new bond graph method (BGM) model to describe the thin-walled elastic tube with the inviscid compressible fluid is presented. This model represents an improvement over the classical BGM model because the fluid-structure interaction, which acts an important pole in the modal analysis of the pipe with fluid, is considered in this model. The new model is applied to a short water-filled copper pipe to calculate the modal, and comparison is made with the Finite Element Method (FEM) method. Chart results are presented. This model can calculate the modal for the thin-walled elastic tube with fluid well.

In this paper, the law of change of aircraft center of gravity (CG) during fuel consumption is studied. The change of aircraft CG is discussed based on three different structures, such as single tank system, dual-symmetrical tank system and dual-asymmetrical tank system. Based on dual-symmetrical tank system, two fuel consumption modes are considered including the same consumption rate and different consumption rate on both tanks. The curves of change of aircraft CG on different conditions are obtained. The character and trend of change of CG is analyzed.

Efficient detection of bearing failure is one of the most investigated engineering issues in several industries. The proposed method, based on High Frequency Resonance Technique (HFRT) allows detecting precise location of bearing's failure. The location of resonance area is computed by screening the signal's frequency spectrum. Some statistical features are adopted to get a preliminary inspection of the raw signal. This method is validated with Seeded Fault Test Data provided by Case Western Reserve University Bearing Data Center.

Rolling bearing is a kind of very common mechanical components, of which the fault diagnosis is of great significance. In former fault diagnosis of bearings power spectrum is widely used. In this paper, a method consists of power spectrum analysis and support vector machine is proposed. Experimental results show that this method can be effectively applied in rolling bearing fault diagnosis.

This paper focuses on the fault-tolerant operation mechanism and failure mechanism of Avionics Full Duplex Switched Ethernet (AFDX). With the strong correlation among airborne systems, fault tolerance mechanism of full duplex communication and dynamic adjustment strategy, this paper presents an integrated reliability theory and evaluation methodology based on service performance and fault tolerant degradation. In order to describe the correlation among hardware, software and protocol, stochastic Petri network is utilized to establish their reliability model based on failure coverage rate. Directing to the sudden traffic variance under failures, this paper presents an optimal threshold traffic model based on sudden traffic and steady traffic, in which the particle swarm algorithm will be used to get the optimization threshold. With the control theory, this paper will design the nonlinear robust resources controller to adjust the buffer size dynamically. Selecting the time delay as service performance, G/M/1 queuing model is explicated to analyze the traffic shaping and the network calculus algorithm is used to get the maximum network time delay. Combing the time delay of traffic shaping and adjustment, the service-oriented integrated reliability model of AFDX is presented based on time delay and structure reliability to realize the reliability evaluation for airborne network - AFDX of commercial airplane.

This paper presents a novel adaptive fuzzy robust control (AFRC) algorithm for a class of nonlinear systems with partly linearly parameterized system models, unknown system nonlinearities and external unknown disturbances. For linearly parameterized system models, discontinuous-projection-based adaptive control law is used to estimate system parameters. The Takagi-Sugeno fuzzy logic systems are used to approximate unknown system nonlinearities. Robust control law ensures the robustness of closed-loop control system. This proposed AFRC effectively combines techniques of adaptive control and fuzzy control and it improves control performance by retaining the advantages of both. A systematic design procedure of AFRC is developed by combining the backstepping technique and small-gain approach. The closed-loop stability is studied using small gain theorem and the control system is proved to be semi-globally uniformly ultimately bounded.

In order to make the Electric servo meet the system reliability index and have best performance parameters, a optimization design method of Electric servo based on dynamic performance and reliability is proposed. This method includes three steps. The first step is the reliability prediction. Through the reliability prediction, the weak parts of the Electric servo can be found, and the system reliability model can be also established to find the reliability constraints parameters. The second step is the dynamic performance modeling, through the dynamic performance model, which we can get dynamic performance constraints parameters. The last step is optimization design. With reliability constraints parameters and dynamic performance constraints parameters, the common parameters of the reliability and the dynamic performance can be discovered. After that, establishing a target function, drawing the Constraint conditions of the parameters, and using the particle swarm optimization to optimize the common parameters. By adjusting the parameters, the Electric servo can finally meet the system reliability index and have the best performance parameters.

A special detection device, or an E-Pod is generally installed in a plane to perform the target detection and track mission. Aimed at designing a reliable E-Pod, This paper adopts the SolidWorks to build a physical model of E-Pod, and analyze its strength, stiffness and frequency to assess the feasibility for further improvement. Then this paper offers a way of optimization to ensure the reliability of the E-Pod.

Wireless Sensor Network (WSN) has attracted many concerns in different domains because of its special features. A WSN is a set of a large number of resource constrained sensor nodes which have abilities for information detection, data processing, and short-range radio communication. WSN can be used for both military applications and civil applications with the tasks such as detection and monitoring of significant events in different environments or large areas. How to improve the reliability of WSN is one of the essential challenges for WSN from theoretical research to actual application. Five hot research interests have been discussed respectively in this paper, and several relevant state-of-the-art technologies have been analyzed, then, future direction and critical technologies are proposed as well. In addition, a Generalized Stochastic Petri nets (GSPN) model is designed to evaluate the reliability of a parallel-redundant fault tolerant WSN. GSPN model is more observable, scalable and portable than Markov model.

Solid lubricated bearing is a key component in space mechanisms, and its performance degradation assessment is very important due to high reliability demands of space mechanisms. In this context, performance degradation assessment is carried with experimental method. Vibration signal and friction torque signal are used to make the assessment. Both results are close to that of dismantling after the experiment.

Transmission on-off valve is an important component in the aircraft fuel system. It is directly related to the fuel supply security during the flight. Erosive wear is the main form of transmission on-off valve failure. it is said that Mathematical modeling of the dynamic characteristics of hydro-mechanical shut-off valves in typical aircraft fuel system, and the failure to model caused by Erosive wear.

Axial piston pump is an important component in hydraulic system, which is widely applied in aeronautic hydraulic energy system. The performance of piston pump directly influences the operation of hydraulic system. This study puts emphasis up on the common failure mechanism analysis of pump. Then the model of piston pump was established in AMESim. Considering the characteristic of the fault condition, a set of qualitative parameter was chose and injected into the model. Fault simulation was conducted and the diagram was got and well explained.

Directing to the nonlinear factors existed in hydraulic servo system, this paper focuses on fuzzy PID controller whose scaling factors and membership functions are optimized by evolutionary algorithm simultaneously to achieve high performance. With the improved genetic algorithm of sub population and dynamic time variant mutation, evolutionary algorithm can search the optimal state effectively. The scaling factors map the variable into universe of discourse while the tuning of scaling factors and fuzzy membership functions. Application of hydraulic servo system illustrates the effectiveness of the proposed technique.

This paper presents the controller design with robust and high precision performance based on novel evolutionary algorithm. Robust fuzzy PID controller is developed with the aim to achieve precise tracking performance. Mathematical model is developed that includes LuGre dynamic friction model to accounts for main nonlinearities. The influence of controller parameters on control performance is determined with statistical approach. To acquire the global optimum these parameters are optimized by using novel evolutionary algorithm. Effectiveness and robustness of the proposed controller are compared with fuzzy PID control. Extensive simulation results confirm robust fuzzy PID method is marvelous with high effectiveness, robustness and precise tracking performance under unknown modeling error, parametric uncertainties and external disturbances.

This paper presents an integrated reliability definition and mathematical metrics based on function reliability and performance reliability, in which the function reliability highlights failure causality from system to components and the performance reliability focuses on the dynamic performance degradation with operational time. Based on the simulation techniques, this paper establishes the simulation template for typical control system that can describe the dynamic process of performance in continuous time until the parameters exceed the designated threshold. Considering the influence of monitoring voting system, this paper establishes the Markov model with fault detection rate, isolation rate and false alarm rate, investigates the influence to whole system reliability of such factors. Application of triple redundant actuator indicates that the integrated reliability assessment makes full use of the advantages on dynamic fault tree analysis, Markov Model and control performance simulation, and solves the difficulties on calculating reliability considering the characteristics of monitoring system and performance degradation.