| Popis: |
Centrifugal pump, which is widely used in water conservancy, electric power, petrochemical, ship, aerospace, and other technical fields, is the core equipment used to ensure all kinds of energy transfer. Cavitation not only affects the service life of the centrifugal pump but also seriously impacts the reliability of the process flow or device system. Due to the influence of the life, position and number of vibration sensors, the existing cavitation fault feature identification accuracy is not enough. The state analysis and characteristic recognition of the current signal under the cavitation state of the centrifugal pump are conducted in this paper based on soft sensing technology, and the signal component and judgment threshold representing the cavitation state are obtained. The following results are presented. Under different critical cavitation numbers, a small number of bubbles appeared near the suction surface of the inlet edge of the impeller, which verified the reliability of criterion for the critical cavitation number when the head coefficient decreased by 3%. The overall accuracy of binary classification cavitation recognition based on the current signal is 12.9% higher than that of three classification cavitation recognition. The recognition rate of VMD decomposition under different working conditions is higher than that of EMD, in design conditions, for example, overall accuracy improved by 7.3%, which also indicates that the obtained cavitation information of each component by VMD decomposition is richer than that obtained by EMD decomposition. Comparing different working conditions, a large flow rate easily leads to cavitation and high recognition current rate, compared with the flow of 0.75 Q and 1.25 Q, the overall accuracy is improved by 9.6%. |
