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Ye et al. [[5]] proposed a simultaneous fault diagnosis method based on hierarchical multi-label classification and a sparse Bayesian extreme learning machine to identify the single failure and simultaneous failures of the main reducer and determine the probability of each failure mode. 10.3390/app13084655 4 Gu Y., Zhang Y., Yang M., Li C. Motor On-Line Fault Diagnosis Method Research Based on 1D-CNN and Multi-Sensor Information. Gu et al. [[4]] proposed a model for motor fault diagnosis based on deep learning and multi-sensor information fusion, including the steps of data layer fusion, feature layer fusion, and fault classification, and finally realized the fast and accurate diagnosis of motor faults. [Extracted from the article]

In this paper, the hydraulic efficiency optimization calculation method of a ten-stage centrifugal pump is researched. According to the hydraulic loss model, a multi-objective optimization calculation method based on surrogate models is proposed. In order to study the highly nonlinear relationship between key design variables and centrifugal pump external characteristic values, this paper builds the quadratic response surface, the radial basis Gaussian response surface, and Kriging three surrogate models using computer fluid dynamics (CFD) simulation analysis. Two types of calculation models (hydraulic loss model and three surrogate models) combined NSGA-Π genetic algorithm are applied to optimize the key design variables and to find the optimal solution of each model. The accuracy and effectiveness of the efficiency optimization methods based on the two types of calculation models are compared and analyzed. The results show that the calculation method of hydraulic loss model based on the semitheoretical and semi-empirical formula is less time-consuming but inaccurate. In contrast, the optimization method based on surrogate models using CFD simulation is accurate. What's more, comparing the surrogate models, the results based on the complete quadratic response surface model which make the efficiency of the first stage centrifugal pump reach 77.26% are more accurate. [ABSTRACT FROM AUTHOR]

In rotating machinery, the malfunctions of rolling bearings are one of the most common faults. To prevent machine breakdown, the pattern recognition of rolling bearing faults has been a pivotal issue for fault identification and classification. This study proposes a new feature extraction method based on ensemble empirical mode decomposition (EEMD) and singular value decomposition (SVD) for fault classification. The proposed E–S method (EEMD combined with SVD using feature parameters) intends to enhance the faults identification capability in different working conditions, including various fault types (FT), fault severities (FS), and fault loads (FL). In this study, the E–S method is adopted to analyze the simulated signals. And the experiment further discusses three cases of different FT, FS, and FL separately under six different classifiers. The experimental results show that different fault classes can be effectively distinguished by the proposed E–S in comparison with other traditional feature extraction methods. Hence, the proposed method is verified to have an effective and excellent performance in bearing fault classification. [ABSTRACT FROM AUTHOR]