Research on prediction method of sensorless measurement of centrifugal pump operational state based on hybrid model

Abstract

Centrifugal pumps are widely used in various fields such as industrial production and urban water supply. In order to achieve the monitoring of operational state of pumping systems, as well as the problems of inability to install sensors or high cost of sensors in practical engineering, the sensorless estimation method of centrifugal pump operational state provides a new opportunity for the monitoring and control of pumping systems. In this paper, a hybrid model based on QP model and neural network model is proposed to estimate the flow rate of rational pump by dividing the speed region. Taking a centrifugal pump as the research object, the operational points of the pump at different rotational speeds are acquired by experiments. Using the proposed method, a prediction model is established to predict the operational state of the pump. The prediction results are verified through experiments, and the error analysis of the prediction results is carried out. The results show that the proposed hybrid model can improve the prediction accuracy of centrifugal pump operational state and has certain practical value of engineering.