Centrifugal pump degradation identification based on GA-GMDH algorithm

Objective In order to monitor the health status of a centrifugal pump in real time, this study proposes a model for the real-time identification of the degradation state of centrifugal pumps.

Methods First, based on the operating parameters and degradation mechanism of the centrifugal pump, a combined weighting model using a combination of subjective and objective weights is used to calculate the combined weights, then a health index during the degradation process of the centrifugal pump is constructed. Second, based on the existing pump degradation data, a degradation identification model based on the genetic algorithm-group method of data handling (GA-GMDH) algorithm is proposed.

Results The reliability of the GA-GMDH monitoring model is relatively high, with a root mean square error of 0.029216 between the output values of the health index and the actual values. Based on the model's output results, the accuracy of degradation state identification is 93.333%.

Conclusion The results of this study can provide valuable references for the health monitoring and maintenance operation management of centrifugal pumps.