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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]