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The enhancement of the sample set quality and the improvement of the deep learning model are aimed to be achieved through a comprehensive approach, designed to increase diagnostic accuracy. This is necessitated by the challenges posed by gear fault vibration signals, characterized by nonlinearity, non-stationarity, sample imbalance, and variable operating conditions. The signals were processed initially using VMD (variational mode decomposition), whereby energy entropy dimensionless indicators of each IMF (intrinsic mode function) component were extracted as the sample set. This approach is intended to counteract the detrimental effects of sample imbalance and operational variations. Subsequently, the learning rate of CNN (convolutional neural network) was optimized autonomously using PSO (particle swarm optimization) algorithm, aiming to minimize the risk of model overfitting. Additionally, a multi-branch global average pooling network, incorporating the concept of Inception modules, was designed for the purpose of feature fusion, thereby seeking to enhance the fault diagnostic accuracy of the model. The effectiveness of the method is validated through experimental data, demonstrating that fault diagnosis accuracy of up to 0. 99 is achievable with the proposed method. Compared with other methods, the effectiveness and stability of this approach are highlighted. [ABSTRACT FROM AUTHOR]

Lithium batteries have the advantages of high energy density and long cycle life, and are widely used in electric vehicle power plants. However, the operating conditions of vehicles are complex and variable, and the battery exhibits highly nonlinear properties, making it difficult to accurately calculate the state of charge (SOC) of the battery. In order to optimize the SOC estimation accuracy of lithium batteries, a fractional second-order RC model combined with Warburg elements was constructed, and a adaptive genetic algorithm was used for parameter identification. Combining multi innovation theory and extended Kalman filter filter algorithm, an ion battery SOC estimation algorithm based on multi innovation extended Kalman filter (MIEKF) was proposed, and the effectiveness of this method was verified by experimental data, which provided a new approach and practical support for improving the SOC estimation accuracy and the cycle life of vehicle mounted lithium batteries [ABSTRACT FROM AUTHOR]

A common way to research bearing fault excitation is to establish dynamic model of bearing spalling fault. Since the contact situation of the rolling element is more complex when it passes through the fault zone, the factor that has a great influence on the correctness of the bearing fault dynamics model is to accurately establish the time-varying excitation function of the bearing spalling fault. However, the existing simplified excitation function is difficult to accurately express the actual contact between rolling elements and faults when solving bearing round fault. Therefore, the round spalling fault of the outer ring of rolling bearing is studied. The time- varying displacement excitation model was established based on the elastic deformation caused by round spalling fault and the transient position of the rolling element passing through the fault area. The variation law of the contact gap when the rolling element passes through the round spalling fault zone of the outer ring was studied, the time interval characteristics between double shock signals gener- ated by different fault diameters were analyzed, and the feasibility of the model was verified through simulation and experiment, which provides theoretical support for the research of bearing spalling fault. [ABSTRACT FROM AUTHOR]

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