Aiming at the influence of mixed noise of bearing vibration signal on the extraction of useful information, a fault diagnosis optimize classifier based on multi-scale permutation entropy (MPE) and cuckoo search algorithm (CS) is proposed. Firstly, the MPE threshold method is adopted to select the appropriate variational mode decomposition algorithm (VMD) parameters, and then the signal is reconstructed by adding neutral white noise, and the reconstructed signal is decomposed by MPE-OVMD algorithm to obtain the optimal IMF component. Finally, the cuckoo search algorithm is used to optimize the global optimal solution of the support vector machine, thereby achieving the classification model of support vector machine with the best parameters. The analysis results of motor signals show that the method can eliminate the phenomena of mode aliasing and signal over-decomposition. An analytical comparison of the CSSVM classifier is carried out with the performance of the learners such as recall rate, ROC curve, AUC. The contrast experiment shows that the classification model can avoid misrecognition of the fault sample as the normal condition and maximum the optimal maintenance time of the equipment under the premise of ensuring the accuracy. The classifier model of the cuckoo optimization algorithm has better fitting accuracy than others such as the Grid Search algorithm (GS), Particle Swarm Optimization (PSO), Genetic Algorithm search (GA), and the ensemble fault recognition rate is as high as 90%.

The mechanical fault diagnosis results of the high voltage circuit breakers (HVCBs) are mainly determined by the feature vector and classifier used. In order to obtain more remarkable characteristics of signals and a robust classifier which is suitable for small sample classification, in this paper, a new mechanical fault diagnosis method is proposed. Firstly, the vibration signals of HVCBs are collected by a designed acquisition system, and the noise of signals is eliminated by a soft threshold de-noising method. Secondly, the empirical wavelet transform (EWT) is adopted to decompose the signals into a series of physically meaningful modes, and then, the improved time-frequency entropy (ITFE) method is used to extract the characteristics of the vibration signals. Finally, a generalized regression neural network (GRNN) is used to identify four types of vibration signals of HVCBs, while the smoothing parameter $\delta$ of GRNN is optimized by a loop traversal method. The experimental results show that by using this optimal classifier for fault diagnosis, the proposed fault diagnosis method has the better generalization performance and the recognition rate of unknown samples is over 95%, and the signal features obtained by the ITFE method are more significant than those of the traditional TFE method.

In mechanical fault diagnosis of the high voltage circuit breakers (HVCBs), it is often expected that the fault type should be confirmed in time to avoid delaying the best time for mechanical fault diagnosis. The traditional diagnosis method of HVBC is not so profound for the identification of slight faults and does not consider the impact of the recall rate of fault samples on the fault diagnosis results. In this paper, we propose a method for HVCBs mechanical fault diagnosis utilizing variational mode decomposition (VMD) based on improved time segment energy entropy (ITSEE) and a new hybrid classifier. Firstly, the signal is decomposed into K intrinsic mode functions (IMFs) via VMD to establish a component matrix. Secondly, the ITSEE method is used to calculate the energy entropy of the matrix in time domain and frequency domain, so as to better extract the features of slight fault types. Finally, an optimal hybrid classifier model combined of one-class support vector machine (OCSVM) and probabilistic neural network (PNN) is used to identify four types of vibration signals of HVCBs. The experimental results show that the accuracy of unknown samples is 98.75%, and the recall of fault type is 100%. The experimental results show the effectiveness of the method and have important application value for the diagnosis of HVBCs.

Traditional fault diagnosis methods of DC (direct current) motors require establishing accurate mathematical models, effective state and parameter estimations, and appropriate statistical decision-making methods. However, these preconditions considerably limit traditional motor fault diagnosis methods. To address this issue, a new mechanical fault diagnosis method was proposed. Firstly, the vibration signals of motors were collected by the designed acquisition system. Subsequently, variational mode decomposition (VMD) was adopted to decompose the signal into a series of intrinsic mode functions and extract the characteristics of the vibration signals based on sample entropy. Finally, a united random forest improvement based on a SPRINT algorithm was employed to identify vibration signals of rotating machinery, and each branch tree was trained by applying different bootstrap sample sets. As the results reveal, the proposed fault diagnosis method is featured with good generalization performance, as the recognition rate of samples is more than 90%. Compared with the traditional neural network, data-heavy parameter optimization processes are avoided in this method. Therefore, the VMD-SampEn-RF-based method proposed in this paper performs well in fault diagnosis of DC motors, providing new ideas for future fault diagnoses of rotating machinery.

In order to overcome the lack of the multispectral image (MS) and adequately preserve the spatial information of panchromatic (PAN) image and the spectral information of MS image, this study proposes a method which adds the spectral information of the prior MS to the prior PAN during training, and only the posterior PAN is needed for predicting. Firstly, we introduce the autoencoder model based on image colorization and discuss its feasibility in the field of multi-band remote sensing image pan-sharpening. Then, the image quality evaluation functions including spatial and spectral indexes are formed as the loss function to control the image colorization model. Because the loss function contains spatial and spectral evaluation indexes, it could directly calculate the loss between the network output and the label considering characteristics of remote sensing images. Besides, the training data in our model is original PAN, this means that it is not necessary to make the simulated degraded MS and PAN data for training which is a big difference from most existing deep learning pan-sharpening methods. The new loss function including the spectral and spatial quality instead of the general MSE (mean square error), only the original PAN instead of the simulated degraded MS + PAN to be inputted, only the spectral feature instead of the direct fusion result to be learned, these three aspects change the current learning framework and optimization rule of deep learning pan-sharpening. Finally, thousands of remote sensing images from different scenes are adopted to make the training dataset to verify the effectiveness of the proposed method. In addition, we selected seven representative pan-sharpening algorithms and four widely recognized objective fusion metrics to evaluate and compare the performance on the WorldView-2 experimental data. The results show that the proposed method achieves optimal performance in terms of both the subjective visual effect and the object assessment.