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.

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 problems of poor time–frequency (TF) aggregation and severe noise interference when traditional TF analysis (TFA) methods deal with complex multi-component signals, this paper proposes a new TFA method—local maximum synchrosqueezing reassigning chirplet transform (LMSRCT). The core idea of the method is to introduce the principle of general linear chirplet transform (CT) into synchro-reassigning transform, followed by reassigning the results of local maximum synchrosqueezing CT, and then introducing the innovative synchro-reassigning operator. This results to a novel three-step method for extracting instantaneous frequency (IF), which ultimately yields the final TF representation. This method ensures the integrity of each IF, solves the problem of energy divergence problems, and improves aggregation. Simulation and experimental results show that the LMSRCT can provide better characterisation results compared to other methods and can effectively solve the different situations that occur between the IFs of complex signals. The proposed method can effectively estimate transmission speed, which achieves fault diagnosis under tacholess conditions, and the order analysis results are more accurate and reliable.