单腔单振子压电泵流量自测量方法研究

为满足压电泵系统流体精确控制及小型化的需要,提出利用压电自感知及神经网络技术的单腔单振子压电泵流量自测量方法。压电泵结构上采用双压电晶片振子同时作为执行元件与传感元件使用。首先分析了流量影响因素,并分析了传感压电片输出信号与振子变形关系。得出:压电泵流量与振子振动状态存在函数关系,振子的振动状态可由传感压电信号实时反映,因此在信号中隐含着压电泵流量信息。据此制作了传感压电信号参数测量电路样机,将参数测量结果作为神经网络输入,建立了用于压电泵流量预测的BP神经网络模型。实验结果表明:利用该方法得到的预测值与实验测量值之间相关系数在0.999 3以上,最大相对误差率小于3.46%,预测结果与测量值接近。该流量自测量方法具有较好的准确性。

Flow Self-measurement Method for Single Chamber and Single Vibrator Piezoelectric Pump

Aiming at the accurate fluid control and the miniaturization of piezoelectric pump system, a flow self-measurement method for single chamber and single vibrator piezoelectric pump was proposed based on piezoelectric self-sensing and neural network. A bimorph vibrator was applied in piezoelectric pump acting as actuator and sensor simultaneously. Firstly, the influencing factors of flow were analyzed using the pi theorem of hydromechanics. Secondly, the relationship between sensing piezoelectric signal and deformation of the vibrator was studied. Then, the information of flow implicit in the sensing piezoelectric signal was found. Thus, a prototype of the parameter measurement circuit for sensing piezoelectric signal was made, and a BP neural network model for flow prediction was constructed by using the parameters acquired from the circuit as the input vector. Experimental results show that the correlation coefficient between the predicted data and the measured data is above 0.9993, and the maximum relative error is less than 3.46%. the results of prediction are close to the actual data. The method proposed for flow self measurement has a good accuracy.