变转速泵控液压缸系统的多模型自适应PID控制

变转速泵控液压缸系统转速或负载大范围变动导致的液压系统参量非线性变化,可使基于线性模型设计的控制系统出现控制参数不合理、精度不稳定,甚至控制失稳等问题,本文通过分析典型变转速泵控液压缸系统的状态空间,提出了一种多模型自适应PID(Multi-model adaptive PID,MMA-PID)控制方法。该方法根据系统压力的改变引起的油液体积弹性模量的非线性变化,用多个线性子模型分别描述系统行为,并针对每个子模型,单独设计一个合理的控制器。控制过程中,通过卡尔曼滤波器分别估计各个子模型的输出权重系数,并将所有子模型控制输出加权融合作为系统最终的控制输入。仿真和实验结果表明,在工况大范围变化时,多模型自适应PID控制相较于传统的PID控制,能更好的适应系统参量的非线性变化,具有更好的控制效果和动态性能。

基于改进的非线性自抗扰算法的电液伺服力加载控制

针对起落架作动筒磨合与寿命实验台阀控缸力加载系统瞬态和动态加载精度较低,不能满足载荷谱精度要求从而影响作动筒安全性与可靠性判断的问题,从控制算法出发,提出一种具有更高精度及抗干扰能力的改进非线性自抗扰控制算法(Improved nonlinear active disturbance rejection control,INADRC)。首先,建立电液伺服力加载系统AMESim/Simulink联合仿真模型。其次,设计INADRC控制器并使用遗传粒子群算法对其参数寻优。最后,通过对3种目标跟踪信号仿真和实验来验证控制器性能。仿真及实验结果表明,改进非线性自抗扰控制相比于PID控制、非线性自抗扰控制和其他改进非线性自抗扰控制,平均精度分别提升了4.15%,1.15%和0.65%,体现出伺服力瞬态和动态加载精度高、抗干扰能力强的特点。

基于相轨迹图的轴向柱塞泵振动特性研究

考虑到柱塞泵壳体的振动既包含运动状态信息也包含能量状态信息,提出采用柱塞泵壳体振动相轨迹图示化方法揭示柱塞泵振动信号与运行状态间的关系。首先,在分析柱塞泵流体振动传递路径,阐明高压流体激励与泵壳体振动速度、位移及振动能量之间的关系。其次,依据振动频率溯源结果,采用多重同步压缩变换方法(Multi-synchrosqueezing Transform,MSST)对柱塞泵壳体振动加速度信号进行重构,经积分变换后可进一步得到振动速度和位移信息。最后,利用振动速度和位移信息构建壳体振动相轨迹图,并通过实验揭示了不同工况下壳体振动相轨迹图中蕴含的柱塞泵动能、势能变化规律。结果表明,结合振动溯源,MSST能够准确提取壳体振动加速度信号中液压激励引起的振动频率和相位信息。柱塞泵运行工况对柱塞泵壳体动能及势能有显著影响。随系统压力增大,相轨迹与垂直零位移轴的距离明显增大,壳体势能显著增加;随转速增加,相轨迹图的最大振动速度明显增大,壳体动能显著增加。轴向柱塞泵相轨迹图可以更直观地显示壳体动能及势能变化规律。本研究为液压泵、马达等关键元件的故障诊断和健康评估提供了理论依据和方法支持。

Analysis of pressure pulsation mechanism and dynamic characteristics of axial piston pump

The pressure pulsation of axial piston pump is not only an important cause of rotation speed fluctuation,vibration noise and output stability of the hydraulic system,but also the main information source for obtaining fault information.Hydraulic system is characterized by strong noise interference,which leads to low signal-to-noise ratio(SNR)of detection signals.Therefore,it is necessary to dig deep into the system operating state information carried by pressure signals.Firstly,based on flow loss mechanism of the plunger pump,the mapping relationship between flow pulsation and pressure pulsation is analyzed.After that,the pressure signal is filtered and reconstructed based on standard Gabor transform.Finally,according to the time-domain waveform morphology of pressure signal,four characteristic indicators are proposed to analyze the characteristics of pressure fluctuations under different working conditions.The experimental results show that the standard Gabor transform can accurately extract high-order harmonics and phase frequencies of the signal.The reconstructed time-domain waveform of pressure pulsation of the axial piston pump contains a wealth of operating status information,and the characteristics of pulsation changes under various working conditions can provide a new theoretical basis and a method support for fault diagnosis and health assessment of hydraulic pumps,motors and key components.

Monitoring method of gear teeth failure of hydraulic gear pump based on improved VMD and DBN-DNN of electrical signal

Abundant system operation state information is included in the electrical signal of the hydraulic system motor.How to accurately extract and classify the operation information of electrical signal is the key to realize the condition monitoring of hydraulic system.The early fault characteristics of hydraulic gear pump hidden in the motor current signal are weak and difficult to extract by traditional time-frequency analysis.Based on the correlation coefficient and artificial bee colony algorithm(ABC),the parameter optimization of variational mode decomposition(VMD)is realized in this paper.At the same time,the principle of maximum signal correlation coefficient and kurtosis value is adopted to determine the effective intrinsic mode function(IMF).Moreover,the permutation entropy(PE)and root mean square(RMS)of the effective IMF components are input into the deep belief network(DBN-DNN)as high-dimensional feature vectors.The operation state of gear pump is monitored.The results show that the weak characteristics of current signal of gear pump fault are accurately and stably extracted by this method.The running state of gear pump is monitored and the accuracy of gear fault diagnosis is improved.