Rolling bearing is an important part guaranteeing the normal operation of rotating machinery, which is also prone to various damages due to severe running conditions. However, it is usually difficult to extract the weak fault characteristic information from rolling bearing vibration signals and to realize a rolling bearing fault diagnosis. Hence, this paper offers a rolling bearing fault diagnosis method based on successive variational mode decomposition (SVMD) and the energy concentration and position accuracy (EP) index. Since SVMD decomposes a vibration signal of a rolling bearing into a number of modes, it is difficult to select the target mode with the ideal fault characteristic information. Comprehensively considering the energy concentration degree and frequency position accuracy of the fault characteristic component, the EP index is proposed to indicate the target mode. As the balancing parameter is crucial to the performance of SVMD and must be set properly, the line search method guided by the EP index is introduced to determine an optimal value for the balancing parameter of SVMD. The simulation and experiment results demonstrate that the proposed SVMD method is effective for rolling bearing fault diagnosis and superior to the variational mode decomposition (VMD) method.

Gearboxes are widely used in drive systems of rotating machinery. The health status of gearboxes considerably influences the normal and reliable operation of rotating machinery. When a gearbox experiences tooth failure, a vibration signal with impulse features is excited. However, these impulse features tend to be relatively weak and difficult to extract. To solve this problem, a novel approach for gearbox fault feature extraction and fault diagnosis based on improved variational mode extraction (VME) is proposed. Since the initial value of the desired mode center frequency and the value of the penalty parameter in VME must be assigned, a short-time Fourier transform (STFT) was performed, and a new index, the standard deviation of differential values of envelope maxima positions (SDE), is proposed. The feasibility and effectiveness of the proposed approach was verified by a simulation signal and two datasets associated with a gearbox test bench. The results demonstrate that the VME-based approach outperforms the variational mode decomposition (VMD) approach.

Accumulation of sanitary refuse, such as flexible cloth-like structures or the so-called rags, inflows through sewage pumps are prone to tangling, ultimately leading to clogging and wear. To prevent this, the ability of sewage pumps to handle wet wipes, rags, and similar flexible materials is a key feature that must be considered as the pumps are designed. Therefore, this paper proposed a multi-objective optimization strategy based on the fluid–structure interaction simulation, Support Vector Regression (SVR), and non-dominated sorting genetic algorithm III (NSGA-III). First, the values of the optimization objectives were obtained by a Coupled Computational Fluid Dynamics-Discrete Element Method (CFD-DEM) approach. Then, SVR was utilized to establish an approximate model between the design variables and the optimization objectives. The NSGA-III was applied to search the Pareto front. Finally, the improved impeller model was selected by adopting technique for order preference by similarity to an ideal solution (TOPSIS) with entropy weight. The results show that the multi-objective optimization method is suitable for the optimization design of sewage pumps. Comparing the numerical calculations of the original pump and the optimized pump, the results show that the optimized head and efficiency increased by 9.7% and 7.13%, respectively. The optimized pump improves the passage rate of the rag and effectively improves the clogging behavior. The wear amount of the optimized pump is significantly reduced by 32.54%.