Semi-open centrifugal pumps are widely used in various fields. However, the tip leakage vortex (TLV) has a malign effect on the impeller flow field. The structure and trajectory of a TLV under different discharge conditions were simulated and are discussed herein. Then, the characteristics of the TLV were analyzed using a new omega vortex identification method. The external characteristic and pressure fluctuation of the simulation and experiment were consistent. A secondary leakage vortex near the blade outlet was formed under the high-discharge condition. A leading-edge overflow phenomenon under the low-discharge condition and led to the formation of a reverse-flow vortex. The interface between the main flow and tip leakage flow moved toward the impeller upstream with decreased discharge. As a result, the peak of the entropy production curve moved upstream, and leading-edge overflow and reverse flow occurred. The tip leakage flow changed the blade pressure distribution, resulting in a decrease in the blade load.

The wide application of rotating machinery has boosted the development of electricity and aviation, however, long-term operation can lead to a variety of faults. The use of different measures to deal with corresponding malfunctions is the key to generating benefits, so it is significant to carry out the fault diagnosis of rotating machinery. In this work, a test bench for single-span rotor bearings was established, three faults, including spindle bending, spindle crack without end loading and spindle crack with end loading, are experimental analyzed with basic mechanical response. Moreover, a diagnosis is performed using a convolutional neural network, according to the differences in mechanical responses of the three faults obtained from experiments. For three faults, the change in the properties of spindle itself results in different axis trajectories and spectra. Compared with spindle bending fault, spindle crack fault not only cause 1×, 2×, 3× frequency component excitation, also 4×, 5× frequency component excitation. Additionally, the classification accuracy of the training set and the test set under machine learning for the three types of working conditions is 100%. This indicates that the network can significantly identify signal features so as to make effective fault classification.

A sliding bearing system performs a critical function in a rotating machinery system and provides support for the rotating components. In this paper, a single-rotor experimental rig and a dual-rotor sliding bearing experimental rig were independently established, and the dynamic response of the test systems are investigated. To study the effect of oil pressure levels on the dynamic response of the test systems under the single-rotor system and the dual-rotor system, the vibration response of single-rotor and dual-rotor systems ware analyzed by different oil inlet pressures to study the effect of oil pressure levels on the dynamic response of the test systems under the single-rotor system and the dual-rotor system. Among them, the single rotor system under a large oil pressure system is mostly periodic and more stable. While under different inlet oil pressure conditions applied in the experiments, the dynamic motion of the dual-rotor bearing system is relatively stable in both the subcritical and supercritical speed regions. However, the oil whirl and oil whip of the dual-rotor system were observed at the critical speed, which causes the system into unstable multiperiodic motions. In general, this paper can give some insight into the dynamic response of the sliding bearing system influenced by different oil pressure levels.