基于Matlab的制芯机器人夹持器稳定性研究与分析

由于铸造厂制芯机器人的工作环境恶劣,机器人抓取砂芯的稳定性较差,时常出现掉砂芯、夹伤砂芯的问题。针对上述问题建立以夹持器、气压、机械振动的系统数学模型,采用高阶系统极点在[s]平面的分布法来判定该系统的稳定性。在确保高阶系统极点值不变的情况下,通过改变系统的零点值来观察零点值对系统稳定性、振荡衰减时间的影响。用Matlab的pzmap和impulse函数分别对系统的零点、极点分布和零点值的不同对系统响应进行仿真,运用仿真结果与现场试验进行优化参数,结果表明采用一种刚度与柔度各为1/2的夹持器对制芯机器人的稳定性具有较好优化效果。     

含有蓄能器的液压伺服系统动态特性仿真设计

该文以一类常见的电动液压能源为对象,建立了含有蓄能器的电动液压能源的仿真模型,并对能源的启动特性以及负载工作所要求的多种信号的跟踪能力进行了仿真分析。     

发夹式换热器管束流致振动数值模拟研究

运用计算流体力学方法,采用ANSYS CFX软件对发夹式换热器的壳侧流场进行了三维数值模拟。流场计算中采用多孔介质模型对管束区域进行简化,分析了壳侧流场的速度分布,结果表明:直管段部分的流体湍流强度大于弯管段,且外层管束所在区域为高流速区,受流体冲刷严重。结合流场信息,通过功率谱生成随机激振力,采用ABAQUS软件模拟计算了湍流激振下管束的振动响应,结果显示管束的面外均方根位移远大于面内位移,且弯管部分的振动位移最大。该研究结果可为发夹式换热器的性能分析和优化设计提供参考和依据。     

基于关键路径延时检测的自适应电压缩减技术

为了减小传统的最差情况设计方法引入的电压裕量,提出了一种变化可知的自适应电压缩减(AVS)技术,通过调整电源电压来降低电路功耗.自适应电压缩减技术基于检测关键路径的延时变化,基于此设计了一款预错误原位延时检测电路,可以检测关键路径延时并输出预错误信号,进而控制单元可根据反馈回的预错误信号的个数调整系统电压.本芯片采用SMIC180 nm工艺设计验证,仿真分析表明,采用自适应电压缩减技术后,4个目标验证电路分别节省功耗12.4％,11.3％,10.4％和11.6％.     

Improved synergetic excitation control for transient stability enhancement and voltage regulation of power systems

This paper proposes decentralized improved synergetic excitation controllers (ISEC) for synchronous generators to enhance transient stability and obtain satisfactory voltage regulation performance of power systems. Each generator is considered as a subsystem, for which an ISEC is designed. According to the control objectives, a manifold, which is a linear combination of the deviation of generator terminal voltage, rotor speed and active power, is chosen for the design of ISEC. Compared with the conventional synergetic excitation controller (CSEC), a parameter adaptation scheme is proposed for updating the controller parameter online in order to improve the transient stability and voltage regulation performance simultaneously under various operating conditions. Case studies are undertaken on a single-machine infinite-bus power system and a two-area four-machine power system, respectively. Simulation results show the ISEC can provide better damping and voltage regulation performance, compared with the CSEC without parameter adaptation scheme and the conventional power system stabilizer.     

Design of disturbance observer based on adaptive-neural control for large-scale time-delay systems in the presence of actuator fault and unknown dead zone

This article presents an adaptive neural compensation scheme for a class of large-scale time delay nonlinear systems in the presence of unknown dead zone, external disturbances, and actuator faults. In this article, the quadratic Lyapunov–Krasovskii functionals are introduced to tackle the system delays. The unknown functions of the system are estimated by using radial basis function neural networks. Furthermore, a disturbance observer is developed to approximate the external disturbances. The proposed adaptive neural compensation control method is constructed by utilizing a backstepping technique. The boundedness of all the closed-loop signals is guaranteed via Lyapunov analysis and the tracking errors are proved to converge to a small neighborhood of the origin. Simulation results are provided to illustrate the effectiveness of the proposed control approach.     

基于“天空地”一体化技术的安全监测系统建设与应用

西藏江达县白格村金沙江右岸于2018年10月11日和2018年11月3日先后发生2次大规模滑坡—堰塞湖堵江事件,溃堰洪水对下游拉哇库区不良地质体的稳定性造成不同程度的影响。为保障下游水电站建设安全,对拉哇库区主要不良地质体建立了基于星载InSAR技术、无人机技术和地面传感器实时监测的“天空地”一体化监测预警体系,以多维空间采集技术获取变形信息,通过智能监控平台对信息及时进行处理、分析和可视化呈现,利用平台、短信等方式向相关人员进行分级告警,取得了较好的应用效果。     

起重机械防碰装置试验系统的设计

防碰装置是起重机械重要的安全防护装置,其性能质量直接关系到人员和设备安全。常规的防碰装置性能试验方法难以保证试验数据的精度和可靠性。针对防碰装置的工作特点和试验要求,从试验原理、系统结构、运行逻辑等方面入手,设计一套能模拟防碰装置实际运行工况,自动进行性能试验和数据采集处理,精度高、重复性好的试验系统,可以有效提升防碰装置检测能力。     

Operational time-series data modeling via LSTM network integrating principal component analysis based on human experience

Today’s information technologies involve increasingly intelligent systems, which come at the cost of increasingly complex equipment. Modern monitoring systems collect multi-measuring-point and long-term data which make equipment health prediction a “big data” problem. It is difficult to extract information from such condition monitoring data to accurately estimate or predict health statuses. Deep learning is a powerful tool for big data processing that is widely utilized in image and speech recognition applications, and can also provide effective predictions in industrial processes. This paper proposes the Long Short-term Memory Integrating Principal Component Analysis based on Human Experience (HEPCA-LSTM), which uses operational time-series data for equipment health prognostics. Principal component analysis based on human experience is first conducted to extract condition parameters from the condition monitoring system. The long short-term memory (LSTM) framework is then constructed to predict the target status. Finally, a dynamic update of the prediction model with incoming data is performed at a certain interval to prevent any model misalignment caused by the drifting of relevant variables. The proposed model is validated on a practical case and found to outperform other prediction methods. It utilizes a powerful deep learning analysis method, the LSTM, to fully process big condition monitoring series data; it effectively extracts the features involved with human experience and takes dynamic updates into consideration.     

Benchmark value determination of energy efficiency indexes for coal-fired power units based on data mining methods

The operational optimisation of coal-fired power units is important for saving energy and reducing losses in the electric power industry. One of the key issues is how to determine the benchmark values of the energy efficiency indexes of the units. Therefore, a new framework for determining these benchmark values is proposed, based on data mining methods. First, the energy efficiency key performance indicators (KPIs) associated with the net coal consumption rate (NCCR) were selected based on the domain knowledge. Second, the decision-making samples with minimal NCCR were acquired with the fuzzy C-means (FCM) clustering algorithm, and the corresponding clustering centres were employed as the benchmark values. Finally, based on the support vector regression (SVR) algorithm, the target values of the NCCR were obtained with the KPIs as input, and the energy saving potential was evaluated by comparing the target values with the historical values of the NCCR. An actual on-duty 1000 MW unit was taken as study unit, and the results show that the energy saving potential is remarkable when the operators adjust the KPIs based on the calculated benchmark values.