The novel Co-based superalloys are extensively used in gas-powered and jet engine turbines due to their excellent high-temperature performance, achieved by strengthening the L12-γ′ ordered phase. This review present...The novel Co-based superalloys are extensively used in gas-powered and jet engine turbines due to their excellent high-temperature performance, achieved by strengthening the L12-γ′ ordered phase. This review presents an overview of the research progress on oxidation behavior of Co-based superalloys, including oxidation kinetics, oxides morphology, the formation and spallation of oxide layers, and importantly, the synergistic effects of alloying elements on oxidation resistance—a critical area considering the complex interactions with multiple alloying elements. Additionally, this review compares the oxidation resistance of single crystal versus polycrystalline alloys. The effect of phase interface and dislocations on oxidation behavior is also discussed. While significant progress has been achieved, areas necessitating further investigation include optimizing alloy compositions for enhanced oxidation resistance and understanding the long-term stability of oxide layers. The future prospects for Co-based superalloys are promising as ongoing research aims to address the existing challenges and unlock new applications at even higher operating temperatures.展开更多
The active sound absorption technique excels in mitigating low-frequency sound waves,yet it falls short when dealing with medium and high-frequency sound waves.To enhance the sound-absorbing effect of medium and high-...The active sound absorption technique excels in mitigating low-frequency sound waves,yet it falls short when dealing with medium and high-frequency sound waves.To enhance the sound-absorbing effect of medium and high-frequency sound waves,a novel semi-active sound absorption method has been introduced.This method modulates the surface impedance of a loudspeaker positioned behind the sound-absorbing material,thereby altering the sound absorption coefficient.The theoretical sound absorption coefficient is calculated using MATLAB and compared with the experimental one.Results show that the method can effectively modulates the absorption coefficient in response to varying incident sound wave frequencies,ensuring that it remains at its peak value.展开更多
Metal Additive Manufacturing(MAM) technology has become an important means of rapid prototyping precision manufacturing of special high dynamic heterogeneous complex parts. In response to the micromechanical defects s...Metal Additive Manufacturing(MAM) technology has become an important means of rapid prototyping precision manufacturing of special high dynamic heterogeneous complex parts. In response to the micromechanical defects such as porosity issues, significant deformation, surface cracks, and challenging control of surface morphology encountered during the selective laser melting(SLM) additive manufacturing(AM) process of specialized Micro Electromechanical System(MEMS) components, multiparameter optimization and micro powder melt pool/macro-scale mechanical properties control simulation of specialized components are conducted. The optimal parameters obtained through highprecision preparation and machining of components and static/high dynamic verification are: laser power of 110 W, laser speed of 600 mm/s, laser diameter of 75 μm, and scanning spacing of 50 μm. The density of the subordinate components under this reference can reach 99.15%, the surface hardness can reach 51.9 HRA, the yield strength can reach 550 MPa, the maximum machining error of the components is 4.73%, and the average surface roughness is 0.45 μm. Through dynamic hammering and high dynamic firing verification, SLM components meet the requirements for overload resistance. The results have proven that MEM technology can provide a new means for the processing of MEMS components applied in high dynamic environments. The parameters obtained in the conclusion can provide a design basis for the additive preparation of MEMS components.展开更多
Efficient lubrication of magnesium alloys is a highly challenging topic in the field of tribology.In this study,magnesium silicate hydroxide(MSH)nanotubes with serpentine structures were synthesized.The tribological b...Efficient lubrication of magnesium alloys is a highly challenging topic in the field of tribology.In this study,magnesium silicate hydroxide(MSH)nanotubes with serpentine structures were synthesized.The tribological behavior of AZ91D magnesium alloy rubbed against GCr15 steel was studied under lubricating oil with surface-modified MSH nanotubes as additives.The effects of the concentration,applied load,and reciprocating frequency on the friction and wear of the AZ91D alloy were studied using an SRV-4 sliding wear tester.Results show a decrease of 18.7–68.5%in friction coefficient,and a reduction of 19.4–54.3%in wear volume of magnesium alloy can be achieved by applying the synthetic serpentine additive under different conditions.A suspension containing 0.3 wt.%MSH was most efficient in reducing wear and friction.High frequency and medium load were more conducive to improving the tribological properties of magnesium alloys.A series of beneficial physical and chemical processes occurring at the AZ91D alloy/steel interface can be used to explain friction and wear reduction based on the characterization of the morphology,chemical composition,chemical state,microstructure,and nanomechanical properties of the worn surface.The synthetic MSH,with serpentine structure and nanotube morphology,possesses excellent adsorbability,high chemical activity,and good self-lubrication and catalytic activity.Therefore,physical polishing,tribochemical reactions,and physicalchemical depositions can occur easily on the sliding contacts.A dense tribolayer with a complex composition and composite structure was formed on the worn surface.Its high hardness,good toughness and plasticity,and prominent lubricity resulted in the improvement of friction and wear,making the synthetic MSH a promising efficient oil additive for magnesium alloys under boundary and mixed lubrication.展开更多
3D printing technology enhances the combustion characteristics of hybrid rocket fuels by enabling complex geometries. However, improvements in regression rates and energy properties of monotonous 3D printed fuels have...3D printing technology enhances the combustion characteristics of hybrid rocket fuels by enabling complex geometries. However, improvements in regression rates and energy properties of monotonous 3D printed fuels have been limited. This study explores the impact of poly(vinylidene fluoride) and polydopamine-coated aluminum particles on the thermal and combustion properties of 3D printed hybrid rocket fuels. Physical self-assembly and anti-solvent methods were employed for constructing composite μAl particles. Characterization using SEM, XRD, XPS, FTIR, and μCT revealed a core-shell structure and homogeneous elemental distribution. Thermal analysis showed that PVDF coatings significantly increased the heat of combustion for aluminum particles, with maximum enhancement observed in μAl@PDA@PVDF(denoted as μAl@PF) at 6.20 k J/g. Subsequently, 3D printed fuels with varying pure and composite μAl particle contents were prepared using 3D printing. Combustion tests indicated higher regression rates for Al@PF/Resin composites compared to pure resin, positively correlating with particle content. The fluorocarbon-alumina reaction during the combustion stage intensified Al particle combustion, reducing residue size. A comprehensive model based on experiments provides insights into the combustion process of PDA and PVDF-coated droplets. This study advances the design of 3D-printed hybrid rocket fuels, offering strategies to improve regression rates and energy release, crucial for enhancing solid fuel performance for hybrid propulsion.展开更多
A coupled thermal-hydro-mechanical cohesive phase-field model for hydraulic fracturing in deep coal seams is presented.Heat exchange between the cold fluid and the hot rock is considered,and the thermal contribution t...A coupled thermal-hydro-mechanical cohesive phase-field model for hydraulic fracturing in deep coal seams is presented.Heat exchange between the cold fluid and the hot rock is considered,and the thermal contribution terms between the cold fluid and the hot rock are derived.Heat transfer obeys Fourier's law,and porosity is used to relate the thermodynamic parameters of the fracture and matrix domains.The net pressure difference between the fracture and the matrix is neglected,and thus the fluid flow is modeled by the unified fluid-governing equations.The evolution equations of porosity and Biot's coefficient during hydraulic fracturing are derived from their definitions.The effect of coal cleats is considered and modeled by Voronoi polygons,and this approach is shown to have high accuracy.The accuracy of the proposed model is verified by two sets of fracturing experiments in multilayer coal seams.Subsequently,the differences in fracture morphology,fluid pressure response,and fluid pressure distribution between direct fracturing of coal seams and indirect fracturing of shale interlayers are explored,and the effects of the cluster number and cluster spacing on fracture morphology for multi-cluster fracturing are also examined.The numerical results show that the proposed model is expected to be a powerful tool for the fracturing design and optimization of deep coalbed methane.展开更多
To accurately diagnosemisfire faults in automotive engines,we propose a Channel Attention Convolutional Model,specifically the Squeeze-and-Excitation Networks(SENET),for classifying engine vibration signals and precis...To accurately diagnosemisfire faults in automotive engines,we propose a Channel Attention Convolutional Model,specifically the Squeeze-and-Excitation Networks(SENET),for classifying engine vibration signals and precisely pinpointing misfire faults.In the experiment,we established a total of 11 distinct states,encompassing the engine’s normal state,single-cylinder misfire faults,and dual-cylinder misfire faults for different cylinders.Data collection was facilitated by a highly sensitive acceleration signal collector with a high sampling rate of 20,840Hz.The collected data were methodically divided into training and testing sets based on different experimental groups to ensure generalization and prevent overlap between the two sets.The results revealed that,with a vibration acceleration sequence of 1000 time steps(approximately 50 ms)as input,the SENET model achieved a misfire fault detection accuracy of 99.8%.For comparison,we also trained and tested several commonly used models,including Long Short-Term Memory(LSTM),Transformer,and Multi-Scale Residual Networks(MSRESNET),yielding accuracy rates of 84%,79%,and 95%,respectively.This underscores the superior accuracy of the SENET model in detecting engine misfire faults compared to other models.Furthermore,the F1 scores for each type of recognition in the SENET model surpassed 0.98,outperforming the baseline models.Our analysis indicated that the misclassified samples in the LSTM and Transformer models’predictions were primarily due to intra-class misidentifications between single-cylinder and dual-cylinder misfire scenarios.To delve deeper,we conducted a visual analysis of the features extracted by the LSTM and SENET models using T-distributed Stochastic Neighbor Embedding(T-SNE)technology.The findings revealed that,in the LSTMmodel,data points of the same type tended to cluster together with significant overlap.Conversely,in the SENET model,data points of various types were more widely and evenly dispersed,demonstrating its effectiveness in distinguishing between different fault types.展开更多
The application of machine learning in alloy design is increasingly widespread,yet traditional models still face challenges when dealing with limited datasets and complex nonlinear relationships.This work proposes an ...The application of machine learning in alloy design is increasingly widespread,yet traditional models still face challenges when dealing with limited datasets and complex nonlinear relationships.This work proposes an interpretable machine learning method based on data augmentation and reconstruction,excavating high-performance low-alloyed magnesium(Mg)alloys.The data augmentation technique expands the original dataset through Gaussian noise.The data reconstruction method reorganizes and transforms the original data to extract more representative features,significantly improving the model's generalization ability and prediction accuracy,with a coefficient of determination(R^(2))of 95.9%for the ultimate tensile strength(UTS)model and a R^(2)of 95.3%for the elongation-to-failure(EL)model.The correlation coefficient assisted screening(CCAS)method is proposed to filter low-alloyed target alloys.A new Mg-2.2Mn-0.4Zn-0.2Al-0.2Ca(MZAX2000,wt%)alloy is designed and extruded into bar at given processing parameters,achieving room-temperature strength-ductility synergy showing an excellent UTS of 395 MPa and a high EL of 17.9%.This is closely related to its hetero-structured characteristic in the as-extruded MZAX2000 alloy consisting of coarse grains(16%),fine grains(75%),and fiber regions(9%).Therefore,this work offers new insights into optimizing alloy compositions and processing parameters for attaining new high strong and ductile low-alloyed Mg alloys.展开更多
According to surface morphology,microhardness,X-ray diffraction,and static contact angle experiments,the changes in the surface integrity and corrosion resistance of 6061-T6 aluminum alloy after ultrasonic shot peenin...According to surface morphology,microhardness,X-ray diffraction,and static contact angle experiments,the changes in the surface integrity and corrosion resistance of 6061-T6 aluminum alloy after ultrasonic shot peening(USP)were investigated.Results show that the grain size of the material surface is reduced by 43%,the residual compressive stress has an increasing trend,the roughness and hardness are increased by approximately 211.1%and 35%,respectively.And the static contact angle is increased at first,followed by a slight decrease.Weighing,scanning electron microscope,and energy dispersive spectrometer were used to study the samples after a cyclic corrosion test.Results show that USP reduces the corrosion rate by 41.2%.A model of surface corrosion mechanism of USP is developed,and the mechanism of USP to improve the corrosion resistance of materials is discussed.The introduction of compressive residual stresses,grain refinement,increased grain boundaries,increased hardness,and increased static contact angle are the main factors related to the improvement of corrosion resistance in most materials,while increased roughness tends to weaken surface corrosion resistance.展开更多
To verify the wear resistance and erosion resistance of Ti-doped Ta_(2)O_(5)coating(TTO),a series of TTOs were prepared by magnetron sputtering technology by controlling the power of the Ti target.The change of growth...To verify the wear resistance and erosion resistance of Ti-doped Ta_(2)O_(5)coating(TTO),a series of TTOs were prepared by magnetron sputtering technology by controlling the power of the Ti target.The change of growth structure,microstructure,and tribological properties of TTOs with Ti target power was studied.After the erosion test,the variation of erosion damage behavior of TTOs with mechanical properties under different erosion conditions was further studied.The results show that the TTOs eliminate the roughness,voids,and defects in the material due to the mobility of the adsorbed atoms during the growth process,and a flat and dense smooth surface is obtained.Tribological tests show that the TTOs are mainly characterized by plastic deformation and microcrack wear mechanism.Higher Ti target power can improve the wear resistance of TTOs.Erosion test results reveal that the impact crater,furrow,micro-cutting,brittle spalling,and crack formation are the main wear mechanisms of the TTOs samples under erosion conditions.展开更多
A low rare-earth containing ZEK100-O magnesium alloy was welded to AA1230-clad high-strength AA2024-T3 aluminum alloy via solidstate ultrasonic spot welding(USW)to evaluate the microstructure,tensile lap shear strengt...A low rare-earth containing ZEK100-O magnesium alloy was welded to AA1230-clad high-strength AA2024-T3 aluminum alloy via solidstate ultrasonic spot welding(USW)to evaluate the microstructure,tensile lap shear strength,and fatigue properties.The tensile strength increased with increasing welding energy,peaked at a welding energy of 1000 J,and then decreased due to the formation of an increasingly thick diffusion layer mainly containing Al12Mg17intermetallic compound at higher energy levels.The peak tensile lap shear strength attained at 1000 J was attributed to the optimal inter-diffusion between the magnesium alloy and softer AA1230-clad Al layer along with the presence of‘fishhook'-like mechanical interlocks at the weld interface and the formation of an indistinguishable intermetallic layer.The dissimilar joints welded at 1000 J also exhibited a longer fatigue life than other Mg-Al dissimilar joints,suggesting the beneficial role of the softer clad layer with a better intermingling capacity during USW.While the transverse-through-thickness(TTT)failure mode prevailed at lower cyclic loading levels,interfacial failure was the predominant mode of fatigue failure at higher cyclic loads,where distinctive fatigue striations were also observed on the fracture surface of the softer clad Al layer.This was associated with the presence of opening stress and bending moment near the nugget edge despite the tension-tension lap shear cyclic loading applied.展开更多
Range-azimuth imaging of ground targets via frequency-modulated continuous wave(FMCW)radar is crucial for effective target detection.However,when the pitch of the moving array constructed during motion exceeds the phy...Range-azimuth imaging of ground targets via frequency-modulated continuous wave(FMCW)radar is crucial for effective target detection.However,when the pitch of the moving array constructed during motion exceeds the physical array aperture,azimuth ambiguity occurs,making range-azimuth imaging on a moving platform challenging.To address this issue,we theoretically analyze azimuth ambiguity generation in sparse motion arrays and propose a dual-aperture adaptive processing(DAAP)method for suppressing azimuth ambiguity.This method combines spatial multiple-input multiple-output(MIMO)arrays with sparse motion arrays to achieve high-resolution range-azimuth imaging.In addition,an adaptive QR decomposition denoising method for sparse array signals based on iterative low-rank matrix approximation(LRMA)and regularized QR is proposed to preprocess sparse motion array signals.Simulations and experiments show that on a two-transmitter-four-receiver array,the signal-to-noise ratio(SNR)of the sparse motion array signal after noise suppression via adaptive QR decomposition can exceed 0 dB,and the azimuth ambiguity signal ratio(AASR)can be reduced to below-20 dB.展开更多
It is well known that the kit completeness of parts processed in the previous stage is crucial for the subsequent manufacturing stage.This paper studies the flexible job shop scheduling problem(FJSP)with the objective...It is well known that the kit completeness of parts processed in the previous stage is crucial for the subsequent manufacturing stage.This paper studies the flexible job shop scheduling problem(FJSP)with the objective of material kitting,where a material kit is a collection of components that ensures that a batch of components can be ready at the same time during the product assembly process.In this study,we consider completion time variance and maximumcompletion time as scheduling objectives,continue the weighted summation process formultiple objectives,and design adaptive weighted summation parameters to optimize productivity and reduce the difference in completion time between components in the same kit.The Soft Actor Critic(SAC)algorithm is designed to be combined with the Adaptive Multi-Buffer Experience Replay(AMBER)mechanism to propose the SAC-AMBER algorithm.The AMBER mechanism optimizes the experience sampling and policy updating process and enhances learning efficiency by categorically storing the experience into the standard buffer,the high equipment utilization buffer,and the high productivity buffer.Experimental results show that the SAC-AMBER algorithm can effectively reduce the maximum completion time on multiple datasets,reduce the difference in component completion time in the same kit,and thus optimize the readiness of the part kits,demonstrating relatively good stability and convergence.Compared with traditional heuristics,meta-heuristics,and other deep reinforcement learning methods,the SAC-AMBER algorithm performs better in terms of solution quality and computational efficiency,and through extensive testing on multiple datasets,the algorithm has been confirmed to have good generalization ability,providing an effective solution to the FJSP problem.展开更多
Recent advancements in machine learning and computer vision enable direct prediction of mechanical properties from microstructure images.The feasibility of this process hinges on the material structure-property relati...Recent advancements in machine learning and computer vision enable direct prediction of mechanical properties from microstructure images.The feasibility of this process hinges on the material structure-property relationship,richness of the dataset,and the choice of machine learning approach.This study investigates the application of a deep learning model to directly predict the yield strength(YS),ultimate tensile strength(UTS),and true stress-strain curve of the cast-forged AZ80 alloys from SEM microstructure images.We manufactured 27 cast-forged AZ80 magnesium alloy components using varied process parameters,creating a diverse dataset of AZ80 microstructures and mechanical properties through their characterization.In addition to predicting magnesium alloy properties,we address challenges related to data imbalance,brightness and contrast variability,and microstructure long-range heterogeneity.We demonstrate that synthetic data oversampling using a denoising diffusion probabilistic model effectively improves the model’s prediction accuracy via balancing the minority classes.A rigorous analysis of the model’s performance shows that the model accurately predicts the YS,UTS,and Ramberg-Osgood equation’s parameters(K and n).In image-out validation,the model achieves average percentage errors of 2.10%(YS),2.15%(UTS),1.50%(K),and 5.47%(n).In class-out validation,the errors are 6.27%,9.58%,4.69%,and 10.24%,respectively.展开更多
This study proposes an automatic control system for Autonomous Underwater Vehicle(AUV)docking,utilizing a digital twin(DT)environment based on the HoloOcean platform,which integrates six-degree-of-freedom(6-DOF)motion...This study proposes an automatic control system for Autonomous Underwater Vehicle(AUV)docking,utilizing a digital twin(DT)environment based on the HoloOcean platform,which integrates six-degree-of-freedom(6-DOF)motion equations and hydrodynamic coefficients to create a realistic simulation.Although conventional model-based and visual servoing approaches often struggle in dynamic underwater environments due to limited adaptability and extensive parameter tuning requirements,deep reinforcement learning(DRL)offers a promising alternative.In the positioning stage,the Twin Delayed Deep Deterministic Policy Gradient(TD3)algorithm is employed for synchronized depth and heading control,which offers stable training,reduced overestimation bias,and superior handling of continuous control compared to other DRL methods.During the searching stage,zig-zag heading motion combined with a state-of-the-art object detection algorithm facilitates docking station localization.For the docking stage,this study proposes an innovative Image-based DDPG(I-DDPG),enhanced and trained in a Unity-MATLAB simulation environment,to achieve visual target tracking.Furthermore,integrating a DT environment enables efficient and safe policy training,reduces dependence on costly real-world tests,and improves sim-to-real transfer performance.Both simulation and real-world experiments were conducted,demonstrating the effectiveness of the system in improving AUV control strategies and supporting the transition from simulation to real-world operations in underwater environments.The results highlight the scalability and robustness of the proposed system,as evidenced by the TD3 controller achieving 25%less oscillation than the adaptive fuzzy controller when reaching the target depth,thereby demonstrating superior stability,accuracy,and potential for broader and more complex autonomous underwater tasks.展开更多
A rock-drilling jumbo is the main piece of tunneling equipment used in the energy and infrastructure industries in various countries.The positioning accuracy of its drilling boom greatly affects tunneling efficiency a...A rock-drilling jumbo is the main piece of tunneling equipment used in the energy and infrastructure industries in various countries.The positioning accuracy of its drilling boom greatly affects tunneling efficiency and section-forming quality of mine roadways and engineering tunnels.In order to improve the drilling-positioning accuracy of a three-boom drilling jumbo,we established a kinematics model of the multi-degree-of-freedom(multi-DOF)multi-boom system,using the improved Denavit-Hartenberg(D-H)method,and obtained the mapping relationship between the end position and the amount of motion of each joint.The error of the inverse kinematics calculation for the drilling boom is estimated by an analytical method and a global search algorithm based on particle swarm optimization(PSO)for a straight blasting hole and an inclined blasting hole.On this basis,we propose a back-propagation(BP)neural network optimized by an improved sparrow search algorithm(ISSA)to predict the positioning error of the drilling booms of a three-boom drilling jumbo.In order to verify the accuracy of the proposed error compensation model,we built an automatic-control test platform for the boom,and carried out a positioning error compensation test on the boom.The results show that the average drilling-positioning error was reduced from 9.79 to 5.92 cm,and the error was reduced by 39.5%.Therefore,the proposed method effectively reduces the positioning error of the drilling boom,and improves the accuracy and efficiency of rock drilling.展开更多
For the process of multi-robot collaboration to lift the same lifted object by flexible cables,the existing collision detection algorithm of cables between the environmental obstacles has the problem of misjudgment an...For the process of multi-robot collaboration to lift the same lifted object by flexible cables,the existing collision detection algorithm of cables between the environmental obstacles has the problem of misjudgment and omission.In this work,the collision detection of cable vector was studied,and the purpose of collision detection was realized by algorithm.Considering the characteristics of cables themselves,based on oriented bounding box theory,the cable optimization model and environmental obstacle model were established,and a new basic geometric collision detection model was proposed.Then a fast cable vector collision detection algorithm and an optimization principle were proposed.Finally,the rationality of the cable collision detection model and the effectiveness of the proposed algorithm were verified by simulation.Simulation results show that the proposed method can meet the requirements of the fast detection and the accuracy in complex virtual environment.The results lay a foundation for obstacle avoidance motion planning of system.展开更多
Flow velocity uniformity of the microchannel plate is a major factor affecting the performance of microchannel devices.In order to improve the velocity distribution uniformity of the microchannel plate,we designed two...Flow velocity uniformity of the microchannel plate is a major factor affecting the performance of microchannel devices.In order to improve the velocity distribution uniformity of the microchannel plate,we designed two new microchannel structures:V-type and A-type.The effects of various structural parameters of the manifolds on the velocity distribution are reported.The V-type and A-type microchannel plates had a more uniform velocity distribution compared to the Z-type microchannel plate.The final result showed that it is beneficial for the V-type microchannel plate to obtain a more uniform velocity distribution when the manifold structure parameters are X_(in)=-1,X_(out)=0,Y_(in)=10,Y_(out)=6,Hin=4,H_(out)=1,and R=0.5.展开更多
System upgrades in unmanned systems have made Unmanned Aerial Vehicle(UAV)-based patrolling and monitoring a preferred solution for ocean surveillance.However,dynamic environments and large-scale deployments pose sign...System upgrades in unmanned systems have made Unmanned Aerial Vehicle(UAV)-based patrolling and monitoring a preferred solution for ocean surveillance.However,dynamic environments and large-scale deployments pose significant challenges for efficient decision-making,necessitating a modular multiagent control system.Deep Reinforcement Learning(DRL)and Decision Tree(DT)have been utilized for these complex decision-making tasks,but each has its limitations:DRL is highly adaptive but lacks interpretability,while DT is inherently interpretable but has limited adaptability.To overcome these challenges,we propose the Adaptive Interpretable Decision Tree(AIDT),an evolutionary-based algorithm that is both adaptable to diverse environmental settings and highly interpretable in its decision-making processes.We first construct a Markov decision process(MDP)-based simulation environment using the Cooperative Submarine Search task as a representative scenario for training and testing the proposed method.Specifically,we use the heat map as a state variable to address the issue of multi-agent input state proliferation.Next,we introduce the curiosity-guiding intrinsic reward to encourage comprehensive exploration and enhance algorithm performance.Additionally,we incorporate decision tree size as an influence factor in the adaptation process to balance task completion with computational efficiency.To further improve the generalization capability of the decision tree,we apply a normalization method to ensure consistent processing of input states.Finally,we validate the proposed algorithm in different environmental settings,and the results demonstrate both its adaptability and interpretability.展开更多
This paper introduces a new method based on deep belief networks(DBNs)to integrate intrinsic vibration information and assess the similarity of subspaces established on the Grassmann manifold for intelligent fault dia...This paper introduces a new method based on deep belief networks(DBNs)to integrate intrinsic vibration information and assess the similarity of subspaces established on the Grassmann manifold for intelligent fault diagnosis of a reciprocating compressor(RC).Initially,raw vibration signals undergo empirical mode decomposition to break them down into multiple intrinsic mode functions(IMFs).This operation can reveal inherent vibration patterns of fault and other components hidden in the original signals.Subsequently,features are refined from all the IMFs and concatenated into a high-dimensional representative vector,offering localized and comprehensive insights into RC operation.Through DBN,the fault-sensitive information is further refined from the features to enhance their performance in fault identification.Finally,similarities among subspaces on the Grassmann manifold are computed to match fault types.The efficacy of the method is validated usingfield data.Comparative analysis with traditional approaches for feature dimension reduction,feature extraction,and Euclidean distance-based fault identification underscores the effectiveness and superiority of the proposed method in RC fault diagnosis.展开更多
基金support from the National Natural Science Foundation of China(Nos.52171107,52201203)the Hebei Provincial Natural Science Foundation,China(No.E2021501026)the National Natural Science Foundation of China-Joint Fund of Iron and Steel Research(No.U1960204).
文摘The novel Co-based superalloys are extensively used in gas-powered and jet engine turbines due to their excellent high-temperature performance, achieved by strengthening the L12-γ′ ordered phase. This review presents an overview of the research progress on oxidation behavior of Co-based superalloys, including oxidation kinetics, oxides morphology, the formation and spallation of oxide layers, and importantly, the synergistic effects of alloying elements on oxidation resistance—a critical area considering the complex interactions with multiple alloying elements. Additionally, this review compares the oxidation resistance of single crystal versus polycrystalline alloys. The effect of phase interface and dislocations on oxidation behavior is also discussed. While significant progress has been achieved, areas necessitating further investigation include optimizing alloy compositions for enhanced oxidation resistance and understanding the long-term stability of oxide layers. The future prospects for Co-based superalloys are promising as ongoing research aims to address the existing challenges and unlock new applications at even higher operating temperatures.
基金National Natural Science Foundation of China(No.51705545)。
文摘The active sound absorption technique excels in mitigating low-frequency sound waves,yet it falls short when dealing with medium and high-frequency sound waves.To enhance the sound-absorbing effect of medium and high-frequency sound waves,a novel semi-active sound absorption method has been introduced.This method modulates the surface impedance of a loudspeaker positioned behind the sound-absorbing material,thereby altering the sound absorption coefficient.The theoretical sound absorption coefficient is calculated using MATLAB and compared with the experimental one.Results show that the method can effectively modulates the absorption coefficient in response to varying incident sound wave frequencies,ensuring that it remains at its peak value.
基金funded by the National Natural Science Foundation of China Youth Fund(Grant No.62304022)Science and Technology on Electromechanical Dynamic Control Laboratory(China,Grant No.6142601012304)the 2022e2024 China Association for Science and Technology Innovation Integration Association Youth Talent Support Project(Grant No.2022QNRC001).
文摘Metal Additive Manufacturing(MAM) technology has become an important means of rapid prototyping precision manufacturing of special high dynamic heterogeneous complex parts. In response to the micromechanical defects such as porosity issues, significant deformation, surface cracks, and challenging control of surface morphology encountered during the selective laser melting(SLM) additive manufacturing(AM) process of specialized Micro Electromechanical System(MEMS) components, multiparameter optimization and micro powder melt pool/macro-scale mechanical properties control simulation of specialized components are conducted. The optimal parameters obtained through highprecision preparation and machining of components and static/high dynamic verification are: laser power of 110 W, laser speed of 600 mm/s, laser diameter of 75 μm, and scanning spacing of 50 μm. The density of the subordinate components under this reference can reach 99.15%, the surface hardness can reach 51.9 HRA, the yield strength can reach 550 MPa, the maximum machining error of the components is 4.73%, and the average surface roughness is 0.45 μm. Through dynamic hammering and high dynamic firing verification, SLM components meet the requirements for overload resistance. The results have proven that MEM technology can provide a new means for the processing of MEMS components applied in high dynamic environments. The parameters obtained in the conclusion can provide a design basis for the additive preparation of MEMS components.
基金support from the National Natural Science Foundation of China(grant number 52075544)Innovation Funds of Jihua Laboratory(X220971UZ230)+1 种基金Basic and Applied Basic Research Foundation of Guangdong Province(2022A1515110649)Funds from Research Platforms of Guangdong Higher Education Institutes(2022ZDJS038).
文摘Efficient lubrication of magnesium alloys is a highly challenging topic in the field of tribology.In this study,magnesium silicate hydroxide(MSH)nanotubes with serpentine structures were synthesized.The tribological behavior of AZ91D magnesium alloy rubbed against GCr15 steel was studied under lubricating oil with surface-modified MSH nanotubes as additives.The effects of the concentration,applied load,and reciprocating frequency on the friction and wear of the AZ91D alloy were studied using an SRV-4 sliding wear tester.Results show a decrease of 18.7–68.5%in friction coefficient,and a reduction of 19.4–54.3%in wear volume of magnesium alloy can be achieved by applying the synthetic serpentine additive under different conditions.A suspension containing 0.3 wt.%MSH was most efficient in reducing wear and friction.High frequency and medium load were more conducive to improving the tribological properties of magnesium alloys.A series of beneficial physical and chemical processes occurring at the AZ91D alloy/steel interface can be used to explain friction and wear reduction based on the characterization of the morphology,chemical composition,chemical state,microstructure,and nanomechanical properties of the worn surface.The synthetic MSH,with serpentine structure and nanotube morphology,possesses excellent adsorbability,high chemical activity,and good self-lubrication and catalytic activity.Therefore,physical polishing,tribochemical reactions,and physicalchemical depositions can occur easily on the sliding contacts.A dense tribolayer with a complex composition and composite structure was formed on the worn surface.Its high hardness,good toughness and plasticity,and prominent lubricity resulted in the improvement of friction and wear,making the synthetic MSH a promising efficient oil additive for magnesium alloys under boundary and mixed lubrication.
基金funded by the National Natural Science Foundation of China(Grant No.06101213)the National Natural Science Foundation of China(Grant No.22105160).
文摘3D printing technology enhances the combustion characteristics of hybrid rocket fuels by enabling complex geometries. However, improvements in regression rates and energy properties of monotonous 3D printed fuels have been limited. This study explores the impact of poly(vinylidene fluoride) and polydopamine-coated aluminum particles on the thermal and combustion properties of 3D printed hybrid rocket fuels. Physical self-assembly and anti-solvent methods were employed for constructing composite μAl particles. Characterization using SEM, XRD, XPS, FTIR, and μCT revealed a core-shell structure and homogeneous elemental distribution. Thermal analysis showed that PVDF coatings significantly increased the heat of combustion for aluminum particles, with maximum enhancement observed in μAl@PDA@PVDF(denoted as μAl@PF) at 6.20 k J/g. Subsequently, 3D printed fuels with varying pure and composite μAl particle contents were prepared using 3D printing. Combustion tests indicated higher regression rates for Al@PF/Resin composites compared to pure resin, positively correlating with particle content. The fluorocarbon-alumina reaction during the combustion stage intensified Al particle combustion, reducing residue size. A comprehensive model based on experiments provides insights into the combustion process of PDA and PVDF-coated droplets. This study advances the design of 3D-printed hybrid rocket fuels, offering strategies to improve regression rates and energy release, crucial for enhancing solid fuel performance for hybrid propulsion.
基金Project supported by the National Natural Science Foundation of China(No.42202314)。
文摘A coupled thermal-hydro-mechanical cohesive phase-field model for hydraulic fracturing in deep coal seams is presented.Heat exchange between the cold fluid and the hot rock is considered,and the thermal contribution terms between the cold fluid and the hot rock are derived.Heat transfer obeys Fourier's law,and porosity is used to relate the thermodynamic parameters of the fracture and matrix domains.The net pressure difference between the fracture and the matrix is neglected,and thus the fluid flow is modeled by the unified fluid-governing equations.The evolution equations of porosity and Biot's coefficient during hydraulic fracturing are derived from their definitions.The effect of coal cleats is considered and modeled by Voronoi polygons,and this approach is shown to have high accuracy.The accuracy of the proposed model is verified by two sets of fracturing experiments in multilayer coal seams.Subsequently,the differences in fracture morphology,fluid pressure response,and fluid pressure distribution between direct fracturing of coal seams and indirect fracturing of shale interlayers are explored,and the effects of the cluster number and cluster spacing on fracture morphology for multi-cluster fracturing are also examined.The numerical results show that the proposed model is expected to be a powerful tool for the fracturing design and optimization of deep coalbed methane.
基金Yongxian Huang supported by Projects of Guangzhou Science and Technology Plan(2023A04J0409)。
文摘To accurately diagnosemisfire faults in automotive engines,we propose a Channel Attention Convolutional Model,specifically the Squeeze-and-Excitation Networks(SENET),for classifying engine vibration signals and precisely pinpointing misfire faults.In the experiment,we established a total of 11 distinct states,encompassing the engine’s normal state,single-cylinder misfire faults,and dual-cylinder misfire faults for different cylinders.Data collection was facilitated by a highly sensitive acceleration signal collector with a high sampling rate of 20,840Hz.The collected data were methodically divided into training and testing sets based on different experimental groups to ensure generalization and prevent overlap between the two sets.The results revealed that,with a vibration acceleration sequence of 1000 time steps(approximately 50 ms)as input,the SENET model achieved a misfire fault detection accuracy of 99.8%.For comparison,we also trained and tested several commonly used models,including Long Short-Term Memory(LSTM),Transformer,and Multi-Scale Residual Networks(MSRESNET),yielding accuracy rates of 84%,79%,and 95%,respectively.This underscores the superior accuracy of the SENET model in detecting engine misfire faults compared to other models.Furthermore,the F1 scores for each type of recognition in the SENET model surpassed 0.98,outperforming the baseline models.Our analysis indicated that the misclassified samples in the LSTM and Transformer models’predictions were primarily due to intra-class misidentifications between single-cylinder and dual-cylinder misfire scenarios.To delve deeper,we conducted a visual analysis of the features extracted by the LSTM and SENET models using T-distributed Stochastic Neighbor Embedding(T-SNE)technology.The findings revealed that,in the LSTMmodel,data points of the same type tended to cluster together with significant overlap.Conversely,in the SENET model,data points of various types were more widely and evenly dispersed,demonstrating its effectiveness in distinguishing between different fault types.
基金funded by the National Natural Science Foundation of China(No.52204407)the Natural Science Foundation of Jiangsu Province(No.BK20220595)+1 种基金the China Postdoctoral Science Foundation(No.2022M723689)the Industrial Collaborative Innovation Project of Shanghai(No.XTCX-KJ-2022-2-11)。
文摘The application of machine learning in alloy design is increasingly widespread,yet traditional models still face challenges when dealing with limited datasets and complex nonlinear relationships.This work proposes an interpretable machine learning method based on data augmentation and reconstruction,excavating high-performance low-alloyed magnesium(Mg)alloys.The data augmentation technique expands the original dataset through Gaussian noise.The data reconstruction method reorganizes and transforms the original data to extract more representative features,significantly improving the model's generalization ability and prediction accuracy,with a coefficient of determination(R^(2))of 95.9%for the ultimate tensile strength(UTS)model and a R^(2)of 95.3%for the elongation-to-failure(EL)model.The correlation coefficient assisted screening(CCAS)method is proposed to filter low-alloyed target alloys.A new Mg-2.2Mn-0.4Zn-0.2Al-0.2Ca(MZAX2000,wt%)alloy is designed and extruded into bar at given processing parameters,achieving room-temperature strength-ductility synergy showing an excellent UTS of 395 MPa and a high EL of 17.9%.This is closely related to its hetero-structured characteristic in the as-extruded MZAX2000 alloy consisting of coarse grains(16%),fine grains(75%),and fiber regions(9%).Therefore,this work offers new insights into optimizing alloy compositions and processing parameters for attaining new high strong and ductile low-alloyed Mg alloys.
基金Introduction of Talent Research Start-up Fund of Anhui University of Science and Technology(2022yjrc35)Colleges and Universities Excellent Young Talents Domestic Visit Research Project of Anhui Province(gxgnfx2022006)。
文摘According to surface morphology,microhardness,X-ray diffraction,and static contact angle experiments,the changes in the surface integrity and corrosion resistance of 6061-T6 aluminum alloy after ultrasonic shot peening(USP)were investigated.Results show that the grain size of the material surface is reduced by 43%,the residual compressive stress has an increasing trend,the roughness and hardness are increased by approximately 211.1%and 35%,respectively.And the static contact angle is increased at first,followed by a slight decrease.Weighing,scanning electron microscope,and energy dispersive spectrometer were used to study the samples after a cyclic corrosion test.Results show that USP reduces the corrosion rate by 41.2%.A model of surface corrosion mechanism of USP is developed,and the mechanism of USP to improve the corrosion resistance of materials is discussed.The introduction of compressive residual stresses,grain refinement,increased grain boundaries,increased hardness,and increased static contact angle are the main factors related to the improvement of corrosion resistance in most materials,while increased roughness tends to weaken surface corrosion resistance.
文摘To verify the wear resistance and erosion resistance of Ti-doped Ta_(2)O_(5)coating(TTO),a series of TTOs were prepared by magnetron sputtering technology by controlling the power of the Ti target.The change of growth structure,microstructure,and tribological properties of TTOs with Ti target power was studied.After the erosion test,the variation of erosion damage behavior of TTOs with mechanical properties under different erosion conditions was further studied.The results show that the TTOs eliminate the roughness,voids,and defects in the material due to the mobility of the adsorbed atoms during the growth process,and a flat and dense smooth surface is obtained.Tribological tests show that the TTOs are mainly characterized by plastic deformation and microcrack wear mechanism.Higher Ti target power can improve the wear resistance of TTOs.Erosion test results reveal that the impact crater,furrow,micro-cutting,brittle spalling,and crack formation are the main wear mechanisms of the TTOs samples under erosion conditions.
基金the National Natural Science Foundation of China(Grant No.51971183)supported by OU(Osaka University,Japan)program for multilateral international collaboration research in joining and welding。
文摘A low rare-earth containing ZEK100-O magnesium alloy was welded to AA1230-clad high-strength AA2024-T3 aluminum alloy via solidstate ultrasonic spot welding(USW)to evaluate the microstructure,tensile lap shear strength,and fatigue properties.The tensile strength increased with increasing welding energy,peaked at a welding energy of 1000 J,and then decreased due to the formation of an increasingly thick diffusion layer mainly containing Al12Mg17intermetallic compound at higher energy levels.The peak tensile lap shear strength attained at 1000 J was attributed to the optimal inter-diffusion between the magnesium alloy and softer AA1230-clad Al layer along with the presence of‘fishhook'-like mechanical interlocks at the weld interface and the formation of an indistinguishable intermetallic layer.The dissimilar joints welded at 1000 J also exhibited a longer fatigue life than other Mg-Al dissimilar joints,suggesting the beneficial role of the softer clad layer with a better intermingling capacity during USW.While the transverse-through-thickness(TTT)failure mode prevailed at lower cyclic loading levels,interfacial failure was the predominant mode of fatigue failure at higher cyclic loads,where distinctive fatigue striations were also observed on the fracture surface of the softer clad Al layer.This was associated with the presence of opening stress and bending moment near the nugget edge despite the tension-tension lap shear cyclic loading applied.
基金supported by the National Natural Science Foundation of China under Grant 62301051.
文摘Range-azimuth imaging of ground targets via frequency-modulated continuous wave(FMCW)radar is crucial for effective target detection.However,when the pitch of the moving array constructed during motion exceeds the physical array aperture,azimuth ambiguity occurs,making range-azimuth imaging on a moving platform challenging.To address this issue,we theoretically analyze azimuth ambiguity generation in sparse motion arrays and propose a dual-aperture adaptive processing(DAAP)method for suppressing azimuth ambiguity.This method combines spatial multiple-input multiple-output(MIMO)arrays with sparse motion arrays to achieve high-resolution range-azimuth imaging.In addition,an adaptive QR decomposition denoising method for sparse array signals based on iterative low-rank matrix approximation(LRMA)and regularized QR is proposed to preprocess sparse motion array signals.Simulations and experiments show that on a two-transmitter-four-receiver array,the signal-to-noise ratio(SNR)of the sparse motion array signal after noise suppression via adaptive QR decomposition can exceed 0 dB,and the azimuth ambiguity signal ratio(AASR)can be reduced to below-20 dB.
文摘It is well known that the kit completeness of parts processed in the previous stage is crucial for the subsequent manufacturing stage.This paper studies the flexible job shop scheduling problem(FJSP)with the objective of material kitting,where a material kit is a collection of components that ensures that a batch of components can be ready at the same time during the product assembly process.In this study,we consider completion time variance and maximumcompletion time as scheduling objectives,continue the weighted summation process formultiple objectives,and design adaptive weighted summation parameters to optimize productivity and reduce the difference in completion time between components in the same kit.The Soft Actor Critic(SAC)algorithm is designed to be combined with the Adaptive Multi-Buffer Experience Replay(AMBER)mechanism to propose the SAC-AMBER algorithm.The AMBER mechanism optimizes the experience sampling and policy updating process and enhances learning efficiency by categorically storing the experience into the standard buffer,the high equipment utilization buffer,and the high productivity buffer.Experimental results show that the SAC-AMBER algorithm can effectively reduce the maximum completion time on multiple datasets,reduce the difference in component completion time in the same kit,and thus optimize the readiness of the part kits,demonstrating relatively good stability and convergence.Compared with traditional heuristics,meta-heuristics,and other deep reinforcement learning methods,the SAC-AMBER algorithm performs better in terms of solution quality and computational efficiency,and through extensive testing on multiple datasets,the algorithm has been confirmed to have good generalization ability,providing an effective solution to the FJSP problem.
基金the financial contribution from the Natural Sciences and Engineering Research Council of Canada (NSERC), through their Strategic Partnership Grant STPGP 521551in part by support provided by the Digital Research Alliance of Canada (alliance can.ca)
文摘Recent advancements in machine learning and computer vision enable direct prediction of mechanical properties from microstructure images.The feasibility of this process hinges on the material structure-property relationship,richness of the dataset,and the choice of machine learning approach.This study investigates the application of a deep learning model to directly predict the yield strength(YS),ultimate tensile strength(UTS),and true stress-strain curve of the cast-forged AZ80 alloys from SEM microstructure images.We manufactured 27 cast-forged AZ80 magnesium alloy components using varied process parameters,creating a diverse dataset of AZ80 microstructures and mechanical properties through their characterization.In addition to predicting magnesium alloy properties,we address challenges related to data imbalance,brightness and contrast variability,and microstructure long-range heterogeneity.We demonstrate that synthetic data oversampling using a denoising diffusion probabilistic model effectively improves the model’s prediction accuracy via balancing the minority classes.A rigorous analysis of the model’s performance shows that the model accurately predicts the YS,UTS,and Ramberg-Osgood equation’s parameters(K and n).In image-out validation,the model achieves average percentage errors of 2.10%(YS),2.15%(UTS),1.50%(K),and 5.47%(n).In class-out validation,the errors are 6.27%,9.58%,4.69%,and 10.24%,respectively.
基金supported by the National Science and Technology Council,Taiwan[Grant NSTC 111-2628-E-006-005-MY3]supported by the Ocean Affairs Council,Taiwansponsored in part by Higher Education Sprout Project,Ministry of Education to the Headquarters of University Advancement at National Cheng Kung University(NCKU).
文摘This study proposes an automatic control system for Autonomous Underwater Vehicle(AUV)docking,utilizing a digital twin(DT)environment based on the HoloOcean platform,which integrates six-degree-of-freedom(6-DOF)motion equations and hydrodynamic coefficients to create a realistic simulation.Although conventional model-based and visual servoing approaches often struggle in dynamic underwater environments due to limited adaptability and extensive parameter tuning requirements,deep reinforcement learning(DRL)offers a promising alternative.In the positioning stage,the Twin Delayed Deep Deterministic Policy Gradient(TD3)algorithm is employed for synchronized depth and heading control,which offers stable training,reduced overestimation bias,and superior handling of continuous control compared to other DRL methods.During the searching stage,zig-zag heading motion combined with a state-of-the-art object detection algorithm facilitates docking station localization.For the docking stage,this study proposes an innovative Image-based DDPG(I-DDPG),enhanced and trained in a Unity-MATLAB simulation environment,to achieve visual target tracking.Furthermore,integrating a DT environment enables efficient and safe policy training,reduces dependence on costly real-world tests,and improves sim-to-real transfer performance.Both simulation and real-world experiments were conducted,demonstrating the effectiveness of the system in improving AUV control strategies and supporting the transition from simulation to real-world operations in underwater environments.The results highlight the scalability and robustness of the proposed system,as evidenced by the TD3 controller achieving 25%less oscillation than the adaptive fuzzy controller when reaching the target depth,thereby demonstrating superior stability,accuracy,and potential for broader and more complex autonomous underwater tasks.
基金National Natural Science Foundation of China(No.12472038)Natural Science Foundation of Jiangsu Province(No.BK20230688)+2 种基金Natural Science Foundation of the Jiangsu Higher Education Institutions of China(No.22KJB440004)Key Research and Development Program of Xuzhou(No.KC22404)Research Fund for Doctoral Degree Teachers of Jiangsu Normal University of China(No.22XFRS011).
文摘A rock-drilling jumbo is the main piece of tunneling equipment used in the energy and infrastructure industries in various countries.The positioning accuracy of its drilling boom greatly affects tunneling efficiency and section-forming quality of mine roadways and engineering tunnels.In order to improve the drilling-positioning accuracy of a three-boom drilling jumbo,we established a kinematics model of the multi-degree-of-freedom(multi-DOF)multi-boom system,using the improved Denavit-Hartenberg(D-H)method,and obtained the mapping relationship between the end position and the amount of motion of each joint.The error of the inverse kinematics calculation for the drilling boom is estimated by an analytical method and a global search algorithm based on particle swarm optimization(PSO)for a straight blasting hole and an inclined blasting hole.On this basis,we propose a back-propagation(BP)neural network optimized by an improved sparrow search algorithm(ISSA)to predict the positioning error of the drilling booms of a three-boom drilling jumbo.In order to verify the accuracy of the proposed error compensation model,we built an automatic-control test platform for the boom,and carried out a positioning error compensation test on the boom.The results show that the average drilling-positioning error was reduced from 9.79 to 5.92 cm,and the error was reduced by 39.5%.Therefore,the proposed method effectively reduces the positioning error of the drilling boom,and improves the accuracy and efficiency of rock drilling.
基金the National Natural Science Foundation of China(Nos.51265021 and 51965032)the Gansu Provincial Department of Education:Excellent Graduate Student“Innovation Star"Project(No.2022CXZX-571)。
文摘For the process of multi-robot collaboration to lift the same lifted object by flexible cables,the existing collision detection algorithm of cables between the environmental obstacles has the problem of misjudgment and omission.In this work,the collision detection of cable vector was studied,and the purpose of collision detection was realized by algorithm.Considering the characteristics of cables themselves,based on oriented bounding box theory,the cable optimization model and environmental obstacle model were established,and a new basic geometric collision detection model was proposed.Then a fast cable vector collision detection algorithm and an optimization principle were proposed.Finally,the rationality of the cable collision detection model and the effectiveness of the proposed algorithm were verified by simulation.Simulation results show that the proposed method can meet the requirements of the fast detection and the accuracy in complex virtual environment.The results lay a foundation for obstacle avoidance motion planning of system.
基金supported by Scientific Research Project of Guangdong Provincial Department of Education(2024KQNCX152).
文摘Flow velocity uniformity of the microchannel plate is a major factor affecting the performance of microchannel devices.In order to improve the velocity distribution uniformity of the microchannel plate,we designed two new microchannel structures:V-type and A-type.The effects of various structural parameters of the manifolds on the velocity distribution are reported.The V-type and A-type microchannel plates had a more uniform velocity distribution compared to the Z-type microchannel plate.The final result showed that it is beneficial for the V-type microchannel plate to obtain a more uniform velocity distribution when the manifold structure parameters are X_(in)=-1,X_(out)=0,Y_(in)=10,Y_(out)=6,Hin=4,H_(out)=1,and R=0.5.
文摘System upgrades in unmanned systems have made Unmanned Aerial Vehicle(UAV)-based patrolling and monitoring a preferred solution for ocean surveillance.However,dynamic environments and large-scale deployments pose significant challenges for efficient decision-making,necessitating a modular multiagent control system.Deep Reinforcement Learning(DRL)and Decision Tree(DT)have been utilized for these complex decision-making tasks,but each has its limitations:DRL is highly adaptive but lacks interpretability,while DT is inherently interpretable but has limited adaptability.To overcome these challenges,we propose the Adaptive Interpretable Decision Tree(AIDT),an evolutionary-based algorithm that is both adaptable to diverse environmental settings and highly interpretable in its decision-making processes.We first construct a Markov decision process(MDP)-based simulation environment using the Cooperative Submarine Search task as a representative scenario for training and testing the proposed method.Specifically,we use the heat map as a state variable to address the issue of multi-agent input state proliferation.Next,we introduce the curiosity-guiding intrinsic reward to encourage comprehensive exploration and enhance algorithm performance.Additionally,we incorporate decision tree size as an influence factor in the adaptation process to balance task completion with computational efficiency.To further improve the generalization capability of the decision tree,we apply a normalization method to ensure consistent processing of input states.Finally,we validate the proposed algorithm in different environmental settings,and the results demonstrate both its adaptability and interpretability.
文摘This paper introduces a new method based on deep belief networks(DBNs)to integrate intrinsic vibration information and assess the similarity of subspaces established on the Grassmann manifold for intelligent fault diagnosis of a reciprocating compressor(RC).Initially,raw vibration signals undergo empirical mode decomposition to break them down into multiple intrinsic mode functions(IMFs).This operation can reveal inherent vibration patterns of fault and other components hidden in the original signals.Subsequently,features are refined from all the IMFs and concatenated into a high-dimensional representative vector,offering localized and comprehensive insights into RC operation.Through DBN,the fault-sensitive information is further refined from the features to enhance their performance in fault identification.Finally,similarities among subspaces on the Grassmann manifold are computed to match fault types.The efficacy of the method is validated usingfield data.Comparative analysis with traditional approaches for feature dimension reduction,feature extraction,and Euclidean distance-based fault identification underscores the effectiveness and superiority of the proposed method in RC fault diagnosis.
