In nuclear power systems, centrifugal pumps often need to operate under extreme conditions. However, accurately determining the cavitation status of centrifugal pumps under such extreme conditions is challenging. To improve the recognition accuracy of the three statuses of non-cavitation, incipient cavitation, and severe cavitation while improving the anti-interference capability of the monitoring system, this study extracted cavitation features from centrifugal pumps’ motor current and vibration signals under three different operational conditions. It fused the features using feature-level multi-source information fusion (MSIF) based on the backpropagation neural network (BPNN) or support vector machine (SVM) to construct a cavitation status recognition model and analyzed the results to compare with those of recognition without information fusion. The results show that, compared with one signal source, MSIF can significantly improve the recognition accuracy of cavitation statuses. Combined current and pump casing axial monitoring based on the BPNN is the optimal scheme, with an overall recognition accuracy of 97.3% for all operational conditions, compared to 73.9% for the single current signal and 89.3% for the single casing axial vibration signal. These research results can guide the monitoring of cavitation statuses in practical engineering, as well as timely intervention at incipient cavitations to reduce structural damage to centrifugal pumps and prolong service life.

The self-excited oscillating cavitation jet nozzle (SEOCJN) serves as a crucial component for converting hydrostatic energy into dynamic pressure energy and ensuring optimal hydraulic and cavitation performance of cavitating jets. Thus, it is of crucial significance to understand the cavitation characteristics and the influence law of SEOCJN for its extensive industrial applications. This paper utilizes numerical simulation methods to analyze the dynamic process of cavitation initiation, development, and outlet cavitation performance of SEOCJN. It explores the effects of inlet pressure and flow rate on the frequency characteristics of SEOCJN, and establishes a mathematical relationship between self-excited oscillation frequency and outlet flow frequency. The results indicate that the self-excited oscillation nozzle has an inlet diameter (D1) of 4.7 mm, an outlet diameter (D2) of 12.2 mm, a length (L) of 52 mm, a chamber diameter (D) of 83 mm, an oscillation angle of 120°, and an inlet pressure (Pin) of 4.8 MPa. At these parameters, the frequency of the pulse jet reaches 830.01 Hz, with an internal flow period of approximately 0.0024 s. The maximum vapor volume fraction is found to be located 0.28 m from the outlet of the SEOCJN. Furthermore, the frequency of self-excited oscillation pulse increases with an increase in inlet pressure. These findings provide a theoretical basis for the industrial application of self-excited oscillation cavitation jet nozzles.