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The bearing vibration of wind turbines is nonlinear and non-stationary. To effectively extract bearing vibration signal features for fault diagnosis, a method of feature vector extraction based on variational mode decomposition (VMD) and energy entropy is proposed. In addition, the support vector machine (SVM) classifier is used to identify the types of vibration faults. VMD transforms the constrained variational objective function into the unconstrained one to optimize solution. Compared with the VMD and empirical mode decomposition (EMD), the modal decomposition layer of VMD is less than EMD, no found false modality, and truly reflecting signal components. After the Hilbert transformation, double logarithmic coordinates show that the VMD-based spectral characteristics are significant. VMD is performed on the different types of vibration signals of wind turbines. Therefore, VMD is not that affected by noise and has few decomposition levels. The energy entropy of the normalized four modal components is considered the eigenvalue and classified by SVM, and compared with EMD-based and wavelet db4-based energy entropy eigenvalue extraction methods. Experimental results indicate that the accuracy of the method is higher than those based on EMD and wavelet db4, under the limited sample condition. Thus, a referential diagnostic method is provided for practical applications. • A method of feature vector extraction based on variational mode decomposition (VMD) and energy entropy is proposed. • The time-frequency energy spectrum obtained by VMD is compared with that by empirical mode decomposition (EMD). • Different types of bearing vibration signals of wind turbine are analyzed. • The fault diagnostic identification of the proposed method is compared with EMD-based and wavelet db4-based methods. [ABSTRACT FROM AUTHOR]

Partial discharge (PD) is one of the main diagnosis methods for the insulation aging of large motors. A data compression denoising method based on the empirical mode decomposition (EMD) algorithm is proposed according to the weak and large disturbances of large motor PD signals. A simulation model is constructed to verify the effectiveness of the proposed algorithm. Furthermore, the denoising effect of the proposed algorithm is compared with that of the db2 and db8 wavelets in the experiments. The simulation and experimental results show that a data compression denoising algorithm based on EMD can achieve the same denoising effect with those based on db2 and db8 wavelets. The proposed algorithm is even better in terms of relative error and mean square error. For actual signal processing, the data compression denoising algorithm is better than the other algorithms and does not lose the original signal energy. The PD ultrasonic signals are spectrally analyzed by using HHT, and signal energy distribution in time and frequency can be clearly described. The main PD ultrasonic signal characteristics can be extracted currently from the marginal and Hilbert spectra. The results of this study will be helpful for the diagnosis of PD faults in large motors. [ABSTRACT FROM AUTHOR]

Drought is one of the most costly natural disasters in the world. Understanding the drought characteristics in space and time will help deepen our apprehension of the drought formation and evolution mechanisms. It can also contribute to design monitoring system for drought warning and mitigation. In this study, we analyzed meteorological droughts, using the Standardized Precipitation Index, for Lancang River Basin, Southwest China. The 46-year (1960-2005) daily precipitation observations from 35 meteorological stations in the basin were used to derive the drought index. Spatial patterns and temporal patterns of the drought characteristics at multiple scales were investigated. The results utilizing the Principal Component Analysis and K-means clustering methods suggest that the study area can be divided into four sub-regions geographically with each sub-region having its own distinctive temporal evolution patterns of droughts. The temporal variability of droughts was investigated using the Empirical Mode Decomposition (EMD) analysis and the wavelet method. The EMD analysis showed that more than 60 % of the variance of the drought is associated with intra-decadal fluctuations in precipitation, except for one sub region, represented by the Changdu station. The wavelet transform showed an evolution of the main cycle near 3-7 years for most parts of the study area. [ABSTRACT FROM AUTHOR]