To improve the dynamic characteristics of an electro-hydraulic valve system with an independent double-valve core at the load port, this study derives the system state space equation of the main stage closed-loop system, including the structural characteristics of a pressure-reducing valve, a relief valve, a system pipeline, and a cavity based on the power bond diagram theory. The influence of the listed structural parameters on the dynamic characteristics of the main valve’s position is analyzed by the first-order sensitivity method. Since the pilot stage structural parameters of a feedback control system are crucial to the main valve’s motion characteristics, the influence of a different pilot valve’s port structural characteristics on the electro-hydraulic valve system’s characteristics is also studied. The matching performance, linearity, and control sensitivity are used as optimization objectives, the no-self-excited oscillation is set as a constraint condition, and a pilot valve’s structural parameters are self-optimized. The representative structural parameters are used for sensitivity analysis and test verification. Based on the PIV test, similar tests are conducted for different pilot valve port forms, and the pilot valve’s port structural parameters are tested both before and after parameter optimization. The theoretical and experimental results show that the factors that have a significant influence on the main valve's fretting characteristics include the closed-loop proportional gain, the main valve core friction, the spring preload force, and the liquid capacity of the non-spring control chamber. When the pilot valve’s port is U-shaped, the linearity of the flow gain, control sensitivity, and the fretting characteristics of the main valve are significantly improved.
Obtaining accurate motion state information is the foundation of active movement control systems and advanced driving assistance system. To accurately obtain vehicle motion state information without the high-accuracy dynamic models parameters, this paper proposes a vehicle state estimation algorithm based on Whale Optimization Algorithm-Support Vector Regression (WOA-SVR). A double-layer (longitudinal and lateral) regression estimation structure has been proposed based on analyzing the characteristic of vehicle dynamics. Then, the SVR model is trained on a dataset composed of multiple driving conditions, and the WOA is used to optimize the penalty factor c and kernel function parameter g in the relaxation variable during the training process; Finally, the estimation algorithm was validated through virtual simulation of single line change and frequency sweep tests, as well as ABS braking and double line change actual vehicle tests. The results show that this algorithm effectively improves estimation accuracy and is robust to changes in speed.
Purpose This paper aims to study the tribological characteristics of the electrical contact system under different displacement amplitudes. Design/methodology/approach First, the risk frequency of real nuclear safety distributed control system (DCS) equipment is evaluated. Subsequently, a reciprocating friction test device which is characterized by a ball-on-flat configuration is established, and a series of current-carrying tribological tests are carried out at this risk frequency. Findings At risk frequency and larger displacement amplitude, the friction coefficient visibly rises. The reliability of the electrical contact system declines as amplitude increases. The wear morphology analysis shows that the wear rate increases significantly and the degree of interface wear intensifies at a larger amplitude. The wear area occupied by the third body layer increases sharply, and the appearance of plateaus on the surface leads to the increase of friction coefficient and contact resistance. EDS analysis suggests that oxygen elements progressively arise in the third layer as a result of increased air exposure brought on by larger displacement amplitude. Originality/value Results are significant for recognizing the tribological properties of electrical connectors in nuclear power control systems. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-03-2024-0098/
System uncertainty, external disturbances and complex nonlinearities are the main obstacles for high precision control of HESVs. Due to good anti-disturbance capability and low model dependence, linear active disturbance rejection control (LADRC) is popular in engineering practice. However, when LADRC is applied to HESVs, LADRC has limited anti-disturbance capability for high-pressure gas flow force, slow response speed and small response bandwidth, and quickly causes step oscillations. In this paper, the reasons for the remarkable phenomenon of step oscillations and the limited anti-disturbance capability of LADRC are theoretically analyzed and then proved by simulations and experiments. Subsequently, a new robust anti-disturbance control method (RI-ESO) is proposed for the HESV to solve the problem of step oscillations and further improve the dynamic response and anti-disturbance capability. The RI-ESO combines an extended state observer (ESO) and a robust controller. The partial total disturbance is estimated by ESO and then compensated online. The robust integrated controller (RI) can further compensate the remaining nonlinearities and external disturbances to achieve better control performance. The introduction of ESO can reduce the parameter limitations of the robust controller. Various simulations and experiments demonstrate that the RI-ESO can greatly improve the control performance and anti-disturbance capability of HESVs, and effectively avoid step oscillations.
In response to the problems of multi-source influences,high quality feature extraction and selection,complexity and nonlinearity in identification methods for energy consumption proces-ses,a method for identifying and predicting energy consumption of machine tools was proposed by combining integrated models and deep learning.Taking CNC milling as an example,an energy con-sumption model was established based on different cutting periods,and signals were preprocessed by wavelet transform.The preprocessed signals were used to train and predict the energy consumption of the model combining RF and LSTM neural network(RF-LSTM model).Meanwhile,the RF was used to identify the cutting stages and realize the energy consumption classification prediction.The effec-tiveness and superiority of the proposed method were demonstrated through practical cases,and the RF-LSTM model was used to compare with the other four schemes,which verify that this recognition method may accurately predict different operating states and energy consumption of the machine tools.
Based on the advantages of non-contact, flexibility, and small heat-affected zone, laser processing technology is widely used in material removal, addition, modification and other manufacturing fields. However, the emission of large amounts of smoke during laser processing of metal materials will result in environmental pollution in the job shop. Additionally, the processing time of laser equipment becomes uncertain as the power decays with prolonged use. Therefore, this paper is mainly focus on a multi-objective flexible job shop fuzzy green scheduling problem to minimize the fuzzy makespan, fuzzy total energy consumption and fuzzy total smoke emission. To solve it, an improved multi-objective particle swarm optimization algorithm has been designed. Firstly, a preventive maintenance strategy is proposed to reduce the makespan and equipment failure frequency by considering periodic power attenuation of laser equipment. Next, different particle updating strategies are employed to enhance particles’ exploration capabilities. Subsequently, five neighborhood structures are introduced to improve the performance of local search. Finally, comparison experiments are conducted with an expanded common benchmark, and a production case study in a special vehicle body-in-white prototype job shop. The results effectively demonstrate the feasibility and effectiveness of the developed model and improved multi-objective particle swarm optimization algorithm.
在1300℃温度和30 min保温时间的条件下,使用Nb基钎料对铌合金进行钎焊来成形铌合金蜂窝.对铌合金蜂窝与气凝胶填充铌合金蜂窝进行加热试验,测定了热面与冷面的瞬态温度.在与实际试验条件基本保持一致的情况下,建立了金属蜂窝与气凝胶填充金属蜂窝的有限元模型,并对不同几何参数的蜂窝进行传热模拟,分析了蜂窝芯格壁厚、芯体高度、芯格内径对隔热性能的影响,并以此总结出蜂窝的隔热机理.研究结果表明:气凝胶可有效地提高蜂窝隔热性能;芯格壁厚越小,芯体高度越大,蜂窝隔热性能越好;填充气凝胶时,芯格内径越大隔热性能越好,蜂窝芯空腔时,芯格内径越大,隔热性能越差.
为解决大型船用柴油机曲轴箱轴端漏油问题,通过仿真和试验对气液两相条件下迷宫密封的泄漏行为开展研究.基于FLUENT软件进行迷宫密封流场仿真,利用离散相模型开展油滴逃逸行为分析,揭示迷宫密封在气液两相环境中的密封机理和泄漏规律.在试验器上模拟了曲轴箱密封的实际结构和工况条件,测量了不同转速条件下的漏油速率,研究了密封装置中的不同结构特征的功能作用,最终提出了两种改进措施并验证了措施的有效性.研究结果表明:交错迷宫结构与直通型迷宫结构相比可以更显著地减少空气泄漏,增加交错迷宫结构可大幅减少空气对液相介质的向外输运,此外,合理利用迷宫结构进行回油亦能显著减少滑油泄漏.
For the national key R＆D program “high-speed precision suspension bearing”, the requirements for the drop capacity of TDBs were: rotor mass ≥3000 kg, drop speed ≥3000 r/min, and successful drops ≥10 times. Based on this, the research and development of TDB’s drop failure mechanism were carried out. Two hybrid TDBs with ceramic balls suitable for drop conditions were proposed: full complement ball without cage scheme and non-full complement ball with cage scheme. A rotor drop simulation model was built including dynamics and thermal. The force and heating processes of the drop processes were simulated. The effectiveness of the simulation model was verified by mounting the TDBs in the test bench for experiments. TDBs without cage are found to have failed severely. The damages of the failed bearing are detected, and it is observed that due to the large friction coefficient between the rolling elements, the rolling elements are seized up, resulting in continuous dry friction between the rolling elements and the raceways, and between the inner ring and the rotor, and serious burns and wear of the inner rings.
Aiming at the problems of excessive number of iterations and time consumption in automatic optimization design, an efficient optimization search model for centrifugal compressor blades was established based on multi-surrogate model optimization algorithm. A global/local model management strategy and a sample filling method were studied. The comprehensive performance of multi-surrogate model and common meta-heuristic optimization algorithms（particle swarm algorithm, multi-island genetic algorithm, etc.）were compared and analyzed, and the effectiveness of the efficient optimization search model was verified. The results of the centrifugal impeller optimization show that the isentropic efficiency is improved by 0.73%, the total pressure ratio is increased by 0.18%, and the surge margin is improved by 1.1%. Compared with the classical particle swarm algorithm, the optimization time may be reduced by 54.9%.
现有复杂产品装配制造成熟度等级评估依赖专家凭经验确定指标权重和指标评分,存在主观性较强、工作量大、耗时长、无法传承评价实例所蕴含的知识等问题.为了提高复杂产品装配制造成熟度等级评估的效率以及客观性,利用成熟度等级评价实例数据,研究基于 BP 人工神经网络和 Ada-Boost算法的制造成熟度等级评估方法.构建复杂产品装配制造成熟度评价指标体系,给出基于模糊评价法和隶属函数的评价指标及成熟度等级达成度量化方法,建立基于 BP 神经网络的复杂产品装配制造成熟度等级评估模型,并使用AdaBoost算法优化成熟度等级评估BP神经网络模型.采用复杂产品分系统装配制造成熟度评价数据集对评估模型进行训练和实验,分析 BP-AdaBoost 的评估结果,获得最优评价模型.实验结果表明,基于BP-AdaBoost算法的复杂产品装配制造成熟度等级评估方法具有较好的可靠性与准确度.
考虑叶轮真实加工参数,探究了切削速度、每齿进给量、行距、切深、冷却液对整体叶轮叶片的表面完整性(包括表面粗糙度、表面形貌、表面显微硬度、金相组织、残余应力)的影响.结果表明:行距和每齿进给量对叶片表面粗糙度影响最大,行距和每齿进给量越小,叶片表面粗糙度越小,当行距从0.3 mm减小至 0.16 mm时,平均粗糙度Ra 从 0.64 μm减小至 0.48 μm,当每齿进给量从 0.1 mm减小至 0.06 mm时,平均粗糙度从 0.62 μm 减小至 0.4μm;行距和每齿进给量对表面形貌影响最为明显,随着行距和每齿进给量的增大,叶片表面的残留高度增大;切削参数选取相对合理,叶片表面没有产生划痕、划伤和毛刺等缺陷;在合理的参数条件下,叶片表面的力热水平较低,使得叶片表面的显微硬度变化不明显,各切削参数下表面平均硬度在 335HV 左右浮动,同时也没有产生区别于基体的变质层;叶片表层残余应力均表现为压应力,行距、切深对残余应力的影响较小;随切削速度和每齿进给的增大,表层压应力先增大后减小,沿深度方向表现出先微弱减小再增大到峰值最后到达基体的趋势,平行和垂直进给方向上最大残余应力分别可达 275 MPa和 400 MPa;水基冷却液在加工表层达到远大于油基冷却液的残余压应力,但残余应力在深度方向下降明显.
变截面球槽结构表面硬度高、三维形状复杂,采用电解加工具有较好的技术经济优势,但其加工精度和加工稳定性受流场影响比较明显.为提高电解液流场均匀性,基于流场仿真分析探讨了通液方式、流道结构对流场分布的影响规律,并开展了电解加工试验研究.仿真和试验结果表明:采用定向流道供液的分域通液方式有利于提高流场稳定性和流速分布一致性,可以有效提高工艺稳定性;优化出入口流道结构可以进一步提高球槽表面质量,能够满足加工稳定性和加工精度要求.
以含变位的渐开线直齿轮副为研究对象,基于能量法建立了包含非线性赫兹接触刚度、真实的齿廓型线和基体刚度耦合效应的齿轮啮合刚度计算模型.考虑齿轮变位的影响,修正了啮合刚度计算中齿廓型线、基圆半角和齿根圆半角的计算公式;修正了考虑齿面摩擦时的基体耦合刚度模型;推导了考虑几何偏心的齿轮啮合点压力角模型.研究了变位、齿面摩擦和几何偏心对啮合刚度的影响规律.结果表明,变位齿轮副的中心距、啮合刚度幅值、重合度都有着较大变化;由于变位,一对相互啮合的齿轮齿面摩擦力方向改变的时刻不再是发生在单齿啮合区间,进而影响齿轮啮合刚度特征;几何偏心会使啮合刚度峰-峰值增大,同时啮合刚度频域中出现以转频为间隔的边频,当主从动轮都存在偏心时会出现以转频之差为间隔的边频;综合考虑三种因素作用,可显著影响齿轮啮合刚度特性.研究结果为进一步研究变位齿轮动力学提供了参考.
高质量标记数据是基于深度学习的故障诊断方法有效性的重要保障,然而在实际中难以获取大量工业标记故障案例,导致模型的泛化诊断能力弱.针对该问题,提出了动力学仿真数据驱动的域自适应智能诊断方法,该方法考虑仿真数据与实际数据的本质差异,引入了一种特征分离网络域自适应诊断模型,在传统的域自适应模型基础上增加了目标域独有特征提取器以显式分离实际数据中的环境噪声等特征,增强域不变故障特征表示和聚类能力.提出了将域共享特征提取器诊断结果用于域独有特征提取器模型参数的训练策略,进一步提高模型的训练稳定性.采用凯斯西储大学轴承数据集测试了所提方法的诊断性能,结果表明诊断准确率和特征提取及聚类能力均优于其他对比迁移方法,并经验性地分析了模型超参数敏感度.
针对四点接触球轴承钢球与沟道间发生多点接触引起轴承过早失效的问题,以 QJ214 四点接触球轴承为研究对象,建立了钢球-沟道接触模型,分析了结构参数及工况参数变化时钢球-沟道发生多点接触的成因.研究结果表明,内外圈沟道曲率半径系数或内外垫片厚度的增大可使钢球-沟道接触状态由三点接触转变为两点接触,再转变为三点接触;恒定转速时,轴向载荷的减小可使钢球-沟道接触状态由两点接触转变为三点接触;转速及轴向载荷均恒定时,径向载荷的增大可使部分钢球-沟道接触状态由两点接触转变为三点接触,再转变为四点接触.研究成果为避免四点接触球轴承在运转过程中发生多点接触而引起的猫眼圈磨损失效提供了参考.
针对流程制造过程中工艺过程复杂、多工序耦合严重、工艺参数优化困难等问题,提出一种基于长短期记忆(LSTM)神经网络、极限梯度提升(XGBoost)算法和改进粒子群优化(IPSO)算法的多工序工艺参数融合优化方法.基于LSTM神经网络建立了数据预处理模型,通过 LSTM神经网络提取流程工艺数据的时序特征,进而实现了对工艺数据中异常值的处理.在此基础上,通过 XGBoost 算法拟合工艺参数与质量指标间的非线性关系,并结合粒子群算法构建了 PSO-XGBoost 质量预测模型,再将预测模型的输出作为适应度,调用改进粒子群算法反向搜索全局最优工艺参数,得到各工序的最优工艺参数组合,从而实现了流程制造加工质量的融合优化.以某企业的一条流程生产线为例,验证了多工序工艺参数融合优化模型的有效性.
为了提高永磁同步直线电机(PMLSM)的跟随精度,提出了一种基于闭环辨识模型的分数阶反馈控制方法,通过对反馈控制系统误差幅值特性进行特定分数阶次形式的精准调节来有效抑制PMLSM的跟随误差.该方法推导了PMLSM闭环辨识模型,通过误差幅值特性分析,确定了分数阶超前-滞后补偿器参数选定方法,以实现分数阶反馈控制系统对 PMLSM控制频域特性的准确描述,进而提高PMLSM辨识精度及控制性能.采用所提方法在 PMLSM龙门运动平台上进行辨识方法和不同运动规划的跟踪实验及圆轨迹运动实验的验证,实验结果表明,该闭环辨识方法能有效抑制外界对辨识信号的干扰,所建立的闭环辨识模型准确性高;相较于PID控制方法,所提分数阶反馈控制方法能够大幅减小PMLSM跟随误差,对于不同运动规划,跟随误差的均方根误差值减小至少 82.47%,验证了所提方法可有效提高PMLSM跟踪精度.
车辆在加速和爬坡时会产生载荷转移,导致胎压监测结果受到干扰,针对该问题,提出了基于轮胎载荷的胎压监测结果补偿修正方法.基于双重无迹卡尔曼滤波对车辆质量和质心位置进行了双重估计,采用递推最小二乘法(RLS)进行了坡度识别.利用轮胎转毂试验台进行试验,得到载荷、车速、胎压和轮胎下沉量的数据,拟合了载荷、车速、胎压与轮胎下沉量的关系表达式.利用坡度识别,通过车辆质量和质心位置的估计,计算四轮独立载荷,修正相应的轮速脉冲.试验结果表明:没有载荷修正的胎压计算值存在 20%的稳定误差和大幅度波动;加入载荷修正,虽有一定的迟滞性和波动,但会很快收敛到真实值,稳定误差在 5%以内,解决了因载荷变化导致胎压估算不准确的问题.
提出了一种基于位错叠加法和改进概率神经网络的离心泵故障诊断方法以解决现场强背景噪声下基于离心泵声辐射信号的在线故障诊断问题.首先利用位错叠加法对采集的离心泵声辐射信号进行降噪处理,增强声辐射信号中的故障信息,提高信噪比;然后提取声信号时域特征以构造时域特征矩阵,通过主成分分析法对获得的时域特征矩阵进行降维处理,将降维后的信号作为机器学习概率神经网络的输入;同时用哈里斯鹰优化算法来优化概率神经网络参数得到诊断模型,继而用改进的概率神经网络对离心泵故障进行模式识别,并与多种诊断方法进行比较.实验结果表明:位错叠加法能够突出信号特征、实现信号增强,改进的概率神经网络具有良好的离心泵声辐射信号在线故障诊断能力.
