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Aiming at identifying cropland in the Yangtze River Delta, we used unmanned aerial vehicle (UAV) to obtain high spatial and spectral resolution (HSSR) remote sensing images of a small farm in the southern Jiangsu Province. After feature augmentation and compression, we used two 3D-CNN algorithms and the baseline neural network (Baseline-NN) algorithm to classify the UAV-HSSR images. The classification results showed that these three classification methods could achieve the fine scale classification of all elements in the study area, with an overall accuracy of 86.560%, 85.416%, and 94.926%, and Kappa coefficients of 0.846, 0.833, and 0.936, respectively. The findings of this study indicate that hyperspectral UAV images have significant potential in the classification tasks of highly fragmented small farms, although the salt and pepper phenomenon was observed in the results of the three classification methods.

Vibration-based diagnosis is in common use for the health monitoring of rolling element bearing (REB). This paper is concerned with a new defect detection method of the REB under large speed variation by using the correlated ensemble empirical mode decomposition (EEMD) and time-spectral kurtosis (TSK), which are accomplished in two phases. During the first phase, vibration signals are decomposed into intrinsic mode functions (IMFs) with the EEMD, which are then correlated with the original signal in order to determine the shaft IMFs, and hence the distinct instantaneous rotation frequencies (IRFs). During the second phase, the TSK is adopted to determine the fault IMFs, which are further used to reconstruct the fault signal. As a result, the non-stationary signal in the time domain is transformed into the cyclostationary signal in the angular domain with respect to the IRFs by resampling with equal angle increments. Simulations and experiments are carried out to validate the feasibility of the proposed method. It is shown that proposed method offers a potential improvement over the conventional short time Fourier transform and order tracking-based method.

The high frequency resonance (HFR) technique is regarded as a powerful tool for fault diagnosis of rolling element bearings. Different from the usage of the HFR in single fault, the determination of multiple resonant frequency bands under the compound faults and extraneous random impulses is still a challenging task. This paper develops a novel compound fault identification method based on adaptive resonant frequency band extraction. The improved redundant second generation wavelet packet transform is first presented to decompose vibration signal into various narrow bands for providing a fine separation of fault signatures. Then the squared envelope spectrum sparsity criteria is designed to quantify fault characteristics buried in narrow frequency bands. Consequently, the squared envelope spectrum sparsogram is constructed to highlight optimal resonant bands, and the compound faults can be well detected by band-pass filtering and envelope analysis. The numerical and experimental results confirm effectiveness and superiority of the proposed method, which is more sensitive to fault-related impulses and robust to extraneous interferences.