Gearbox oil temperature is one of the important indicators for gearbox condition monitoring and faults early warning. Accurately predicting the gearbox oil temperature change trend can maintain the gearbox in advance and ensure the safety and reliability of the wind turbine gearbox. The purpose of this article is to analyze the supervisory control and data acquisition (SCADA) data in wind turbines.A method based on multi-input improved ant lion optimization and support vector regression (M-IALO-SVR) proposed, which can accurately predict the gearbox oil temperature. The prediction method is compared with back propagation neural network (BPNN) and ALO-SVR methods to verify the effectiveness of the M-IALO-SVR method. To further analyze the prediction results, the 95% confidence interval processing is performed on the residuals of the prediction model, and then the trends of the mean and standard deviation of the moving window residuals are calculated. Testing SCADA data from a wind farm in northeast China, the test results show that when the gearbox is operating normally, the predicted value of the gearbox oil temperature follows the measured value very well. When the gearbox operates abnormally, its temperature deviates from the normal range, and the statistical characteristics of the residuals also change. According to the trend of the residuals statistical characteristics, the abnormal state of the gearbox can be found in time.

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The evaluation of rolling bearing performance degradation has important implications for the prediction and health management (PHM) of rotating equipment. A method for evaluation of rolling bearing performance degradation based on comprehensive index reduction and support vector data description (SVDD) is proposed in this study. Firstly, the improved variational mode decomposition (VMD) method was used to decompose vibration signals, and the defect frequency amplitude ratio index which is sensitive to early faults is extracted. Secondly, a comprehensive feature index set of rolling bearings is constructed by combining traditional time-domain and time–frequency-domain indexes, and the main features are extracted by the dimensionality reduction algorithm of locally linear embedding (LLE). Finally, the SVDD evaluation model was utilized to characterize and evaluate the rolling bearing lifetime degradation process using the distance from the test sample to the trained hypersphere center. Results showed that the proposed comprehensive degradation index can accurately detect the occurrence of early weak fault stage of rolling bearings and objectively reveal the performance degradation process of rolling bearings.

Aiming at the problem of increasing packet loss rate and decreasing throughput caused by the characteristics of high data burst and high channel error rate in the AOS space communication system, this paper considers the factors of queue packet loss and transmission error packet loss and proposes an adaptive modulation coding method based on minimum packet loss rate in the AOS communication system. Firstly, a system packet loss objective function is established, and the modulation coding mode can be determined by solving the objective function minimum. Secondly, the modulation coding mode is dynamically adjusted according to certain rules, determined by the channel state and the queue state jointly. Finally, the system packet loss rate is reduced and the transmission performance of the system is improved. The theoretical analysis and simulation results show that compared with the SLBCCQ method, this method can reduce the system packet loss rate by up to 30%. Meanwhile, compared with the AMC algorithm, it can reduce the system packet loss rate by 41.7%.

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