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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]

An energy analyzing method called the Impact Energy Model (IEM) is introduced to assess and diagnose the rub–impact fault of a rotor system. The main purpose of the model is to indicate the probability of rub–impact or the severity level of the fault. Utilizing the nonlinear predictive energy, an effective quantitative evaluation index IEM is obtained from the model. Some work has been done elsewhere to partly demonstrate the IEM through experiments. In this paper we mainly discuss the variation of the IEM index with the speed ratio Ω at different levels of damping ratio ξ. The stagnation point is discussed in detail which is important to the assessment for the probability or severity of a rub–impact fault. The influence of dynamic stiffness is discussed which may affect the assessment result of the IEM. Furthermore, a sensitive area of the rotor operating system is presented. The result shows that this sensitive area provides a reference for the operation and design of a rotor system. [ABSTRACT FROM PUBLISHER]