Dynamic Characteristics Of System Research Articles

Studies of the Effect of Copper Layer on Dynamic Characteristics of a Stack-Type HTS Maglev System Based on H-Formulation

Studies of the Effect of Copper Layer on Dynamic Characteristics of a Stack-Type HTS Maglev System Based on H-Formulation

Research on large-signal stability of SOFC-lithium battery ship DC microgrid

Aiming at the solid oxide fuel cell (SOFC) applied to the ship DC microgrid in the face of pulse load disturbance is prone to make the SOFC voltage drop too large leading to the DC grid oscillation problem. In this paper, a stability criterion method for SOFC-Li battery DC system based on hybrid potential function is proposed. Firstly, a mathematical model of shipboard DC microgrid with SOFC-Li battery is established and the accuracy of the model is verified. Then, the stability criterion of the system based on the hybrid potential function under large disturbances is constructed. Subsequently, the effects of system stability under impulse load conditions were analysed under different parameters. Based on the constructed criterion, simulation verification of the stability boundary conditions of the SOFC system operating independently or jointly with a lithium battery system is carried out. The experimental results show that the proposed stability criterion and control strategy are effective in accurately predicting the system stability boundary. The experimental results verify the effectiveness of the proposed method in improving the stability of the system and provide a theoretical basis for further research on the dynamic characteristics of SOFC systems under complex load conditions.

Research on Dynamic Characteristics of Two-Stage Gear System with Fatigue Cracks

Research on Dynamic Characteristics of Two-Stage Gear System with Fatigue Cracks

An adaptive feature mode decomposition-guided phase space feature extraction method for rolling bearing fault diagnosis

Abstract The extraction of fault features from rolling bearings is a challenging and highly important task. Since they have complex operating conditions and are usually under a strong noise background. In this study, a novel approach termed phase space feature extraction guided by an adaptive feature mode decomposition (AFMDPSFE) is proposed to detect subtle faults in rolling bearings. Initially, a new method using Kullback-Leiber divergence is introduced to automatically select the optimal mode number and filter length for the decomposition of vibration signals, facilitating the automatic extraction of optimal components and ensuring efficient screening. This eliminates the need for manual configuration of feature mode decomposition parameters. Furthermore, a criterion that could determine two crucial parameters to capture system dynamics characteristics in phase space reconstruction is embedded into AFMDPSFE algorithm. Subsequently, a series of high-dimensional independent components is derived. The envelope spectrum of the principal component exhibiting the highest kurtosis value is computed to achieve fault identification, consequently enhancing the separation of signal from noise. Both simulations and experimental results confirm the effectiveness of AFMDPSFE approach. A comparison analysis shows the excellent performance of AFMDPSFE in extracting fault features from significant noise interference.

Discrete-Time Visual Servoing Control with Adaptive Image Feature Prediction Based on Manipulator Dynamics.

In this paper, a practical discrete-time control method with adaptive image feature prediction for the image-based visual servoing (IBVS) scheme is presented. In the discrete-time IBVS inner-loop/outer-loop control architecture, the time delay caused by image capture and computation is noticed. Considering the dynamic characteristics of a 6-DOF manipulator velocity input system, we propose a linear dynamic model to describe the motion of a robot end effector. Furthermore, for better estimation of image features and smoothing of the robot's velocity input, we propose an adaptive image feature prediction method that employs past image feature data and real robot velocity data to adopt the prediction parameters. The experimental results on a 6-DOF robotic arm demonstrate that the proposed method can ensure system stability and accelerate system convergence.

Effect of tooth surface wear on dynamic characteristics of spur gear transmission system

The meshing stiffness and system dynamic characteristics caused by gear wear fault are studied. Firstly, potential energy method was used to model and solve the meshing stiffness of gears and the effect of wear on the meshing stiffness and static transfer error was analyzed. A 6-dof spur gear dynamics model was established considering various nonlinear factors, and the dynamic responses of the spur gear system under various wear degrees were analyzed by using time domain, spectrum, phase plane and Poincare mapping. Finally, the visual test platform of the gear box is built, and the wear condition and dynamic response of the gear box under different wear cycles are analyzed. Results show that the meshing stiffness of gear decreases due to wear, and the meshing stiffness of double tooth decreases more than that of single tooth. In addition, the static transmission errors caused by wear changes periodically with meshing frequency. Wear causes the vibration response of gear system to become complex gradually, and the nonlinear of the system is enhanced, which changes from single period motion to quasi period motion. Results can provide theoretical support for gear wear reduction, life extension, vibration reduction and noise reduction and lubrication improvement.

Fuzzy Logic and Neural network-based self-tuned PID controller of Quadcopter

Quadcopters have been established as a viable tool for the current era of technology. Quadcopters are also known as drones or unmanned aerial vehicles. The quadcopter can be used for aerial surveys for deliveries, filming, and missions such as detecting and mapping unreachable areas, hills, forests metropolitan areas, rocks, and obstructions that can be a danger or risk to navigation for commercial and security projects. It conducts its survey mission without human operator intervention. After the mission is accomplished, data can be fetched by GPS or the Quadcopter returns to a pre-programmed location where the data can be downloaded and processed. It can lead to an improvement in the position tracking performance due to an intrinsic change in the object's weight, buoyancy, and aerodynamic forces. In addition, quadcopters must navigate the extremely dynamic airborne environment that is represented by urban, rural, metropolitan, or natural environments. In light of this, efficient dock tracking is required to attain the requisite high precision performance, resulting in less time consumption, when the system's dynamic characteristics are time-dependent or the operating conditions and vision guidance alter. The automatic identification of objects by classifying images and self-tuning the gains of a PID (Proportional + Integral + Derivative) controller has been proposed in this paper. The object recognition is based on machine learning guided vision and self-tuning PID controller based on fuzzy logic and neural network.

Fuzzy Logic and Neural network-based self-tuned PID controller of Quadcopter

Quadcopters have been established as a viable tool for the current era of technology. Quadcopters are also known as drones or unmanned aerial vehicles. The quadcopter can be used for aerial surveys for deliveries, filming, and missions such as detecting and mapping unreachable areas, hills, forests metropolitan areas, rocks, and obstructions that can be a danger or risk to navigation for commercial and security projects. It conducts its survey mission without human operator intervention. After the mission is accomplished, data can be fetched by GPS or the Quadcopter returns to a pre-programmed location where the data can be downloaded and processed. It can lead to an improvement in the position tracking performance due to an intrinsic change in the object's weight, buoyancy, and aerodynamic forces. In addition, quadcopters must navigate the extremely dynamic airborne environment that is represented by urban, rural, metropolitan, or natural environments. In light of this, efficient dock tracking is required to attain the requisite high precision performance, resulting in less time consumption, when the system's dynamic characteristics are time-dependent or the operating conditions and vision guidance alter. The automatic identification of objects by classifying images and self-tuning the gains of a PID (Proportional + Integral + Derivative) controller has been proposed in this paper. The object recognition is based on machine learning guided vision and self-tuning PID controller based on fuzzy logic and neural network.

A Comprehensive Review of Alarm Processing in Power Systems: Addressing Overreliance on Fault Analysis and Projecting Future Directions

This paper reviews alarm processing methods in electrical power systems, focusing on evolving strategies beyond traditional fault analysis to accommodate modern grid complexities. Historically, alarm processing has predominantly aimed at fault analysis, increasingly merging with technological advances in communication and computing. However, it still needs to fully meet the challenges posed by the dynamic characteristics of modern power systems. This review points out certain inadequacies in current practices, notably their limited adaptation to new grid conditions. The authors propose a novel generation of alarm processing methodologies designed for future grids, emphasizing managing rare events and enhancing operator decision-making through advanced anomaly detection and explainable artificial intelligence. This synthesis presents a prospective direction for future research and applications in alarm processing, advocating for methodologies better suited to supporting system operators amidst technological advancements.

Assessment of the quality of the labour safety management system in accordance with the requirements of the international standard ISO 45001:2018 at the stage of operation

The subject matter of the article is the assessment of the quality of the occupational safety management system. The goal of the article is to develop a methodology for assessment of the occupational safety management system at the stage of operation. The article solves the following task: to investigate the possibility of applying methods for analysing the dynamic characteristics of the occupational safety management system and to develop algorithms and methods for using the criteria of non-parametric statistics. Methods are used: qualimetry and mathematical statistics. The following results were obtained: an analysis of the requirements of the international standard ISO 45001:2018 was carried out to determine the need to monitor, measure and analyse the functioning of the occupational safety management system, which revealed that the effectiveness of the development and implementation of the occupational safety management system depends on the assessment methodology. An analysis of existing research and publications on the evaluation of processes, including those related to occupational safety, is carried out. The methods for evaluating mathematical systems for processing and controlling gross errors and stationarity, which are key to the analysis of occupational safety management processes, are considered. Non-parametric criteria for checking the stationarity of random processes are considered in detail. Sequences of means and means squares are compared with expected values to determine whether rejections of the stationary hypothesis are justified. The expediency of using these methods for analysing occupational safety management systems is determined. The dynamic nature of the processes and the importance of monitoring and data analysis to ensure effective safety management practices are analysed. Conclusions: The application of methods for analysing the dynamic characteristics of the occupational safety management system is substantiated and algorithms and methods for using the criteria of non-parametric statistics are developed, which allow to obtain an assessment of the system, taking into account the limited information about its indicators and the lack of knowledge of the law of their distribution as a random variable.

Dynamic characteristics of gear-rotor system with gear eccentricity and wear fault

Dynamic characteristics of gear-rotor system with gear eccentricity and wear fault

Dynamic characteristics of rotor system with parallel and angular misaligned involute spline coupling

Dynamic characteristics of rotor system with parallel and angular misaligned involute spline coupling

Dynamic Characteristics of M-Shape Metal Rubber-Coated Pipeline System: Numerical Modeling and Experimental Analysis

As a high-temperature resistant damping material, reducing vibration by coating with M-shape metal rubber (MMR) in a pipeline system is a promising solution due to its energy dissipation induced by micro dry friction between metallic wires. The main challenge for dynamic calculation and performance evaluation of elastic-porous metal rubber (MR) is derived from the intricate spatial network structure. In this work, the dynamic properties including acceleration admittance and insertion loss of the MMR-coated pipeline system were conducted by numerical simulation and experimental analysis. The constitutive models used to characterize hysteresis phenomena, including Yeoh and Bergström–Boyce models, were identified with different density parameters and adopted for steady-state dynamic numerical analysis. The sine sweep frequency test was conducted to verify the accuracy of the developed numerical model. The results indicate that the maximum error of stress–strain curve between numerical prediction and experimental measurement is 10.7%. In the frequency range of 0–1 500[Formula: see text]Hz, the insertion loss of the MMR-coated pipeline system is positively correlated with the density of MMR, as opposed to the coating distance of pipeline clamps and the influence of excitation force is minimal. Furthermore, the error of dynamic response of the pipeline system in low frequency between the experiment and simulation is 4.7%, indicating that the accuracy of the hysteresis model in predicting the dynamic characteristic of MR materials is effective.

Analysis of dynamic characteristics of gear-bearing transmission system with shaft current damage

Analysis of dynamic characteristics of gear-bearing transmission system with shaft current damage

Analysis of the Dynamic Characteristics of Coaxial Counter-Rotating Planetary Transmission System

This paper presents a coaxial counter-rotating planetary transmission system. The transmission system under study is a two-stage planetary gear train (PGT) comprising a fixed-axes PGT and a differential PGT. A dynamic model of the transmission system is established, considering both the excitations caused by the time-varying mesh stiffness (TMS) and the transmission errors, respectively. The Runge–Kutta algorithm is used to calculate and analyze the dynamic characteristics of the system. This includes studying dynamic meshing forces, planet gear displacements, and load-sharing coefficients (LSCs) under both internal and external excitations, as well as different input torques. The results indicate that when considering external excitations, the variations in the meshing force curves become more pronounced. The radial displacements of the planet gears in the differential PGT are greater than that in the fixed-axes PGT. With increasing input torque, the average displacements of the planet gears in all directions tend to increase. The differential PGT, transmitting a higher power, demonstrates a better load-sharing performance compared to the fixed-axes PGT.

Differential privacy Kalman filtering for graphical dynamic systems: Performance improvement and privacy calibration

This paper investigates differential privacy Kalman filtering for graphical dynamic systems. An enhanced differential privacy Kalman filter is initially developed by leveraging the topology, a unique characteristic of graphical dynamic systems, to improve the filtering performance. And then upper and lower error bounds of the enhanced filter are established to evaluate the recipient's ability to infer sensitive data. Based on these error bounds, a privacy calibration guideline is provided to balance privacy and usability of sensitive data. A notable feature of the enhanced filter is that its Kalman gain matrix is a diagonal matrix, which facilitates the independent update of information at each node by reducing the coupling between nodes. This feature diminishes the accumulation of filtering errors, thereby improving the data availability for the recipient. Finally, the effectiveness of the proposed approach is verified through a simulation of epidemiological data analysis, and the results show that the enhanced filter performs well in systems with high privacy levels.

Offset-Free Koopman Model Predictive Control of Thermal Comfort Regulation for a Variable Refrigerant Flow-Dedicated Outdoor Air System-Combined System

Abstract Variable refrigerant flow (VRF) system has been an appealing solution of air conditioning for residential and commercial buildings, due to its flexibility and cost effectiveness, while lack of ventilation capability is a major drawback. Incorporation of dedicated outdoor air system (DOAS) is a typical practice. However, good coordination between DOAS and VRF is critical for achieving desired thermal comfort is challenging due to the possible complexity of mixed sensible and latent heat exchanges. In this paper, to handle the nonlinear dynamic characteristics of VRF-DOAS system, we propose an offset-free Koopman model predictive control (MPC) strategy for thermal comfort regulation, in which the MPC design is computationally more efficient due to the convex problem formulation and the use of reduced-order Koopman models, and the offset-free MPC structure enhances the robustness to model uncertainties and unmeasured disturbances. A control-oriented model is obtained by hybridizing the first-principle and data-driven modeling approach. The proposed controls strategy is evaluated with a Modelica simulation model of a VRF-DOAS system. A Dymola-Python cosimulation platform is developed via the functional mockup interface (FMI), for which the MPC algorithms are implemented in Python. Simulation results show significantly better performance of the offset-free Koopman MPC in thermal comfort regulation.

Vibration response analysis of boneless wiper-windshield system based on multi-body dynamics model

The vibration and noise generated by the boneless wiper-windshield system during its actual operation bother the occupants. Exploring the dynamic characteristics of the boneless wiper-windshield system is a prerequisite for understanding vibration and noise. In this paper, the dynamic characteristics of the boneless wiper-windshield system are investigated, and a rigid-flexible coupled multi-body dynamic model matching the real boneless wiper-windshield system is constructed. The friction characteristic of the dynamic model is studied through the wiper test bench experiment. The nonlinear dynamic response of the dynamic model is calculated with the Adams and results of the frictional vibration are well reproduced in real motions. The parallel analysis of the simulation calculation results and the real vehicle test results confirms the validity of the model. The results show that the vibration generation mechanism at the moments before and after the reversal can be explained by the instability of the wiper blades due to the negative friction characteristic. There are differences in the magnitude of the reversal impact force and the moment of reversal at each position of the wiper blade. The effect of the speed-gears on the system vibration is analyzed through the simulation results. The analysis method and simulation results in this article can provide reference for the research on vibration and noise control of boneless wiper systems.

Representation of solutions to fuzzy linear fractional differential equations with a piecewise constant argument

This study marks the first exploration of fuzzy linear fractional differential equations with a piecewise constant argument (FLFDEs-PCA), incorporating the concept of Caputo’s type gH-differentiability with the order α ∈ (0, 1]. Such problems are noteworthy as they represent hybrid systems, blending the characteristics of continuous and discrete dynamical systems and integrating aspects from both differential and difference equations. The primary objective of this research is to establish a standardized framework for deriving explicit solution formulas for FLFDEs-PCA under various scenarios. Additionally, illustrative examples are provided to demonstrate the practical implications of our theoretical findings.

Effect of Interstage Torsional Damping on Nonlinear Dynamic Characteristics of Two-Stage Planetary Gear System

Abstract This paper explores the influence of interstage torsional damping on the nonlinear dynamics of a two-stage planetary gear transmission system. A comprehensive nonlinear dynamic model is developed, incorporating interstage torsional damping, stiffness, time-varying meshing parameters, damping, and tooth side clearance. The nonlinear equations are derived and solved using the fourth-order Runge–Kutta method. The investigation reveals that the changes in torsional damping between stages significantly affect the nonlinear dynamic characteristics of the system, especially under load fluctuations. Bifurcation characteristics are analyzed using phase diagrams, Poincaré diagrams, time history diagrams, and frequency–domain spectrograms. The simulation results demonstrate that heightened torsional damping between stages distinctly affects the motion states across excitation frequencies. Notably, it mitigates the unstable motion state caused by high-frequency excitation when tooth side clearance is minimal.