The high-speed dual-structure six-phase switched reluctance motor (HDSSRM) exhibits pronounced vibration and acoustic noise, arising from inherent characteristics of the switched reluctance motor (SRM) and the non-uniform circumferential arrangement of its dual-structure six-phase configuration. This paper first analyzes the magnetic flux circuits to compare the effects of long and short magnetic circuits on radial force and operating conditions, and then mitigates the radial force associated with the short magnetic circuit. Next, the Maxwell stress tensor method is employed to examine how modifications to the rotor tooth geometry and the introduction of slots at the stator tooth tips reduce the radial magnetic pull force, and the slot parameters are optimized using a genetic algorithm–optimized back-propagation (GA-BP) neural network. Finally, experiments validate the proposed noise-reduction strategy, and finite-element analysis is used to compare the motor’s torque and radial-force characteristics.
Efficient harvesting of herbaceous mulberry is essential for reducing labor costs and ensuring high-quality stubble for rapid regrowth in sericulture production. However, existing mechanized harvesters rarely enable in situ measurement of cutting and conveying power under field conditions, and the influence of operational parameters on both energy consumption and stubble quality remains insufficiently quantified. In this study, a crawler-type prototype harvester equipped with three independently driven AC servo motors and real-time torque sensors was developed to monitor cutting, conveying, and baling processes. A Central Composite Design (CCD) combined with response surface methodology was employed to investigate the effects of forward speed, conveying speed, and average cutting speed on average cutting power per branch, average conveying power per branch, and stubble quality score. Field trials were conducted in Rizhao, Shandong Province, China, using the mulberry cultivar ‘Guishangyou 12’. The regression models exhibited high goodness of fit (R² = 0.9546∼0.9946) and non-significant lack of fit (p > 0.05). Results indicated that cutting power consumption was on average 3.7 times higher than conveying power, with cutting speed exerting the most significant influence on energy use (p < 0.01) and stubble quality (p < 0.01). The optimal parameter combination—forward speed of 0.55 m·s⁻¹, conveying speed of 0.96 m·s⁻¹, and cutting speed of 0.95 m·s⁻¹—reduced cutting power to 26.91 J·branch⁻¹, minimized conveying power to 6.64 J·branch⁻¹, and achieved a stubble quality score of 9.43. Validation experiments confirmed that deviations from predicted values were <5%. These findings provide a quantitative basis for operational optimization and energy efficiency improvement in herbaceous mulberry harvesting machinery.
Keiichiro Takahashi, Taisuke Kobayashi, Tomoya Yamanokuchi
et al.
This study investigates a novel nonlinear update rule for value and policy functions based on temporal difference (TD) errors in reinforcement learning (RL). The update rule in standard RL states that the TD error is linearly proportional to the degree of updates, treating all rewards equally without any bias. On the other hand, recent biological studies have revealed that there are nonlinearities in the TD error and the degree of updates, biasing policies towards being either optimistic or pessimistic. Such biases in learning due to nonlinearities are expected to be useful and intentionally leftover features in biological learning. Therefore, this research explores a theoretical framework that can leverage the nonlinearity between the degree of the update and TD errors. To this end, we focus on a control as inference framework utilized in the previous work, in which the uncomputable nonlinear term needed to be approximately excluded from the derivation of the standard RL. By analyzing it, the Weber–Fechner law (WFL) is found, in which perception (i.e., the degree of updates) in response to a change in stimulus (i.e., TD error) is attenuated as the stimulus intensity (i.e., the value function) increases. To numerically demonstrate the utilities of WFL on RL, we propose a practical implementation using a reward–punishment framework and modify the definition of optimality. Further analysis of this implementation reveals that two utilities can be expected: i) to accelerate escaping from the situations with small rewards and ii) to pursue the minimum punishment as much as possible. We finally investigate and discuss the expected utilities through simulations and robot experiments. As a result, the proposed RL algorithm with WFL shows the expected utilities that accelerate the reward-maximizing startup and continue to suppress punishments during learning.
Mechanical engineering and machinery, Electronic computers. Computer science
Muhammad Faisal Yaqoob, Christian Gollee, Christer-Clifford Schenke
et al.
The application of robotics has evolved significantly through every industry. Robots do provide a wide range of motion, however their advantage of having lightweight components also limit the rigidity of the tool center point. Compensatory techniques involving joint stiffness determination and model-based predictions is one potential approach while another modern solution is the usage of precision gears. Higher rigidity and lower backlash found in precision gears as compared to conventional gears enable increased accuracy when carrying out production processes with industrial robots. A study at Fraunhofer IWU confirmed this by examining the impact of precision gear on a six-axis robot's accuracy during a milling process. Replacing all gears with precision gear technology or building new robots with them will certainly increase process accuracy. However, with over a half million robots already installed worldwide, there is a definite need to streamline the gear selection while enhancing the accuracy of existing robots with minimal effort and cost. This paper presents a proof of concept to develop a gear selection tool which utilizes robot’s MBS (multibody simulation) model involving gear parameters and process requirements to simplify gear selection for industrial processes. This tool aims to address the question “Which gear(s) needs to be replaced/installed in a robot to achieve the required/improved movement accuracy for an existing or new process?”
In this paper, we introduce a novel method to formally represent elements of control engineering knowledge in a suitable data structure. To this end, we first briefly review existing representation methods (RDF, OWL, Wikidata, ORKG). Based on this, we introduce our own approach: The Python-based imperative representation of knowledge (PyIRK) and its application to formulate the Ontology of Control Systems Engineering (OCSE). One of its main features is the possibility to represent the actual content of definitions and theorems as nodes and edges of a knowledge graph, which is demonstrated by selected theorems from Lyapunov’s theory. While the approach is still experimental, the current result already allows the application of methods of automated quality assurance and a SPARQL-based semantic search mechanism. The feature set of the framework is demonstrated by various examples. The paper concludes with a discussion of the limitations and directions for further development.
Mbanwei Divine Kobbi, Njimboh Henry Alombah, Ngwa Martin Ngwabie
Electric vehicles have advantages such as reduced maintenance and fuel costs compared to internal combustion engines. However, their limited driving range still hinders their widespread adoption compared to internal combustion engines. Harvesting wasted energies through vibrations in electric vehicles is a good approach to complement the energy of their batteries. Space constraints in electric vehicles require devices with high power output per unit volume. This study aimed to design a novel vibration energy harvesting using the geometrical model for electric vehicles. Different configurations and their performance in maximum flux linkage, electromagnetic coupling coefficient, induced voltage, and generated power were investigated. The modeling, excitement, and analysis were conducted using ANSYS Maxwell software with four configurations under similar conditions. These were the Halbach array with three magnets, one coil, and flat back shield; the Halbach array with three magnets and one coil with a stepped back shield; the double magnet array with two magnets, one coil, and flat back shield; and the fourth one was a double magnet array with two magnets, one coil and stepped back shield. The MATLAB Simulink software was used to obtain further results and power output analysis. The results of the analysis show that the Halbach array with three magnets, one coil, and a stepped-back shield is the best configuration for harvesting energy from vibrations, producing an electromagnetic coupling coefficient of up to 110 Wb/m, a voltage of up to 36 V, and generated power density of 0.13 W/cm. A reasonable increase in output using less volume was obtained compared to the other studies. The energy harvested will be applied in future studies to extend the range of agricultural electric vehicles, reducing farmers’ income spent on fuel and maintenance.
Materials of engineering and construction. Mechanics of materials, Mechanical engineering and machinery
Abstract Direct inverse analysis of faults in machinery systems such as gears using first principle is intrinsically difficult, owing to the multiple time- and length-scales involved in vibration modeling. As such, data-driven approaches have been the mainstream, whereas supervised trainings are deemed effective. Nevertheless, existing techniques often fall short in their ability to generalize from discrete data labels to the continuous spectrum of possible faults, which is further compounded by various uncertainties. This research proposes an interpretability-enhanced deep learning framework that incorporates Bayesian principles, effectively transforming convolutional neural networks (CNNs) into dynamic predictive models and significantly amplifying their generalizability with more accessible insights of the model's reasoning processes. Our approach is distinguished by a novel implementation of Bayesian inference, enabling the navigation of the probabilistic nuances of gear fault severities. By integrating variational inference into the deep learning architecture, we present a methodology that excels in leveraging limited data labels to reveal insights into both observed and unobserved fault conditions. This approach improves the model's capacity for uncertainty estimation and probabilistic generalization. Experimental validation on a lab-scale gear setup demonstrated the framework's superior performance, achieving nearly 100% accuracy in classifying known fault conditions, even in the presence of significant noise, and maintaining 96.15% accuracy when dealing with unseen fault severities. These results underscore the method's capability in discovering implicit relations between known and unseen faults, facilitating extended fault diagnosis, and effectively managing large degrees of measurement uncertainties.
AbstractThis paper briefly describes the designing process of a hydraulic inverted pendulum including hardware and software design. First, the mechanical structure, including the components of the platform will be introduced. Second, the electrical system including controllers for receiving signals from sensors which measure the variables important for controlling inverted pendulum is about to be shown. Afterwards, the paper will present a mathematical model of the whole platform, then shows up an open loop simulation established by AMESim and Simulink in order to analysis its dynamic characteristic. By comparing the simulation result and reality, the rationality of mathematical model is finally verified.
The 3 mol% yttria-stabilized tetragonal zirconia polycrystalline (3Y-TZP) ceramics have been extensively used in restorative dentistry due to their excellent esthetic effects, good chemical stability, and superior biocompatibility. Mechanical milling and surface texturing are two important aspects when dealing with the processing of zirconia ceramics for dental applications. The present paper reviews the recent advances achieved in the mentioned research fields. The fundamental milling features of 3Y-TZP ceramics are initially introduced with a particular focus on the cut surface quality. The effects of different process parameters on the machinability of 3Y-TZP ceramics are briefly summarized. Additionally, the basic mechanisms of surface texturing of these bioceramics are introduced. The mechanisms controlling how the microtextures affect the service performances of 3Y-TZP are carefully reviewed. Finally, the current research challenges and the future perspectives concerning the mechanical processing of 3Y-TZP are outlined.
The accessible and convenient hydrogen supply is the foundation of successful materialization for hydrogen-powered vehicles (HVs). This paper proposes a novel optimal scheduling model for gaseous-liquid hydrogen generation and storage plants powered by renewable energy to enhance the economic feasibility of investment. The gaseous-liquid hydrogen generation and storage plant can be regarded as an energy hub to supply concurrent service to both the transportation sector and ancillary market. In the proposed model, the power to multi-state hydrogen (P2MH) process is analyzed in detail to model the branched hydrogen flow constraints and the corresponding energy conversion relationship during hydrogen generation, processing, and storage. To model the coupling and interaction of diverse modules in the system, the multi-energy coupling matrix is developed, which can exhibit the mapping of power from the input to the output. Based on this, a multi-product optimal scheduling (MPOS) algorithm considering complementarity of different hydrogen products is further formulated to optimize dispatch factors of the energy hub system to maximize the profit within limited resources. The demand response signals are incorporated in the algorithm to further enhance the operation revenue and the scenario-based method is deployed to consider the uncertainty. The proposed methodology has been fully tested and the results demonstrate that the proposed MPOS can lead to a higher rate of return for the gaseous-liquid hydrogen generation and storage plant.
Production of electric energy or power. Powerplants. Central stations, Renewable energy sources
When a high impedance fault (HIF) occurs in a distribution network, the detection efficiency of traditional protection devices is strongly limited by the weak fault information. In this study, a method based on S-transform (ST) and average singular entropy (ASE) is proposed to identify HIFs. First, a wavelet packet transform (WPT) was applied to extract the feature frequency band. Thereafter, the ST was investigated in each half cycle. Afterwards, the obtained time-frequency matrix was denoised by singular value decomposition (SVD), followed by the calculation of the ASE index. Finally, an appropriate threshold was selected to detect the HIFs. The advantages of this method are the ability of fine band division, adaptive time-frequency transformation, and quantitative expression of signal complexity. The performance of the proposed method was verified by simulated and field data, and further analysis revealed that it could still achieve good results under different conditions.
Energy conservation, Energy industries. Energy policy. Fuel trade
Abstract This paper investigates the adaptive robust control problem based on reinforcement learning for an affine nonlinear system with unknown time‐varying uncertainty. Inspired by the ability to estimate uncertainty of neural network, a novel policy iteration algorithm is proposed which alternates between the value evaluation, uncertainty estimation, and policy update steps until the adaptive robust control law is obtained. Especially during the step of uncertainty estimation, the unknown time‐varying uncertainty is approximated by a radial basis function neural network and introduce it into the reinforcement learning framework. By designing an appropriate utility function, the algorithm improves both convergence rate and final approximate error comparing with existing reinforcement learning algorithm. The Lyapunov stability theorem provides theoretical demonstrations of the stability and convergence. Furthermore, the uniformly ultimately bounded stability of the affine nonlinear system is demonstrated with unknown time‐varying uncertainty. Finally, the performance of the proposed algorithm is demonstrated through a torsion pendulum system.
Control engineering systems. Automatic machinery (General)
Abstract A three-dimensional conjugate tooth surface design method for Harmonic Drive with a double-circular-arc tooth profile is proposed. The radial deformation function of the flexspline (FS), obtained through Finite Element (FE) analysis, is incorporated into the kinematics model. By analyzing the FS tooth enveloping process, the optimization of the overlapping conjugate tooth profile is achieved. By utilizing the hobbing process, the three-dimensional machinable tooth surface of FS can be acquired. Utilizing the coning deformation of the FS, simulations are conducted to analyze the multi-section assembly and meshing motion of the machinable tooth surface. The FE method is utilized to analyze and compare the loaded contact characteristics. Results demonstrate that the proposed design method can achieve an internal gear pair consisting of a circular spline with a spur gear tooth surface and the FS with a machinable tooth surface. With the rated torque, approximately 24% of the FS teeth are engaged in meshing, and more than 4/5 of the tooth surface in the axial direction carries the load. The contact patterns, maximum contact pressure, and transmission error of the machinable tooth surface are 227.2%, 40.67%, and 71.24% of those on the spur gear tooth surface, respectively. It clearly demonstrates exceptional transmission performance.
Ocean engineering, Mechanical engineering and machinery
Corresponding Author: Nicola Yvonne Bailey Department of Mechanical Engineering, University of Bath, United Kingdom E-mail: n.y.bailey@bath.ac.uk Abstract: Automated machinery and robotics are commonly conventional multibody systems containing bearing components, which exhibit uncertain, discontinuous and complex tribological characteristics. These generate wear and fundamentally limit the precision of small scale motion due to the tribological effects being difficult to compensate for using model-based active control. However, they can be eliminated through the replacement of traditional bearing joints with flexure couplings, which offers a potential increase in the performance envelope. Initially a plain flexure coupling capable of large deformation is investigated, with a representative mathematical model derived based on large deformation Euler-Bernoulli theory which is validated using a bespoke experimental facility; proof of concept for the design of empirical controllers utilising experimental data is presented. Various designs of novel compound flexure couplings are conceived, comprising of multiple sections of spring steel. The presented compound flexure couplings are then characterised experimentally. A focused study of a two-compound flexure coupling-rigid body system is presented and the feasibility of generating open-loop feedfoward controllers from identified models is demonstrated in terms of accurate large displacement control. Including path correction in the presented control methodology reduces tracking errors by at least 62% and 71% in (x, y) directions, respectively, for the cases considered.
Gearbox is the key component of mechanical transmission system. Accurate fault diagnosis of gearbox is of great significance to ensure the operation of rotating machinery. Based on the comprehensive simulation test-bed in the laboratory, a gearbox fault diagnosis method based on QPSO-KELM is proposed. Firstly, the fault pre planting experiments of gear fault, bearing fault and gear bearing mixed fault are carried out on the comprehensive simulation test-bed. Then, the vibration signals collected are preprocessed by TSA to eliminate noise. The time domain, frequency domain and NASA feature parameters of the preprocessed signals are taken as training samples and test samples of QPSO-KELM. The experimental results show that the proposed method can effectively solve the problem of gearbox fault pattern recognition, and the fault diagnosis accuracy is higher than traditional methods, so the research has certain reference significance and engineering application value.
Fariba Moghaddam, A. Vaccari, Thomas Moniquet
et al.
In engineering education, laboratories represent an important academic resource as they provide practical training in addition to the fundamental theories. However, the acquisition of new machinery and the maintenance of the equipment imply a large investment that only a limited number of universities can afford. This paper represents innovative online education activities through a collaborative widespread network with the global south countries, deploying remote laboratories in electrical, mechanical and control engineering at a large scale within MOOC infrastructures.