Fault diagnosis of rolling bearings is significant for mechanical equipment operation and maintenance. Presently, the deep convolutional neural network (CNN) is increasingly used for fault diagnosis of rolling bearings, but CNN has challenges with incomplete training and lengthy training times. This paper proposes a residual network combined with the transfer learning (ResNet-TL) based diagnosis method for rolling bearing, which can preprocess the one-dimensional data of vibration signals into image data. Then, the transfer learning theory in parameter transfer is applied to the training of the network model, and the ResNet34 network is pre-trained and re-trained; the image data are selected to be the inputs of the fault diagnosis model. The experimental validation of the rolling bearing fault dataset collected from the practical bench and Case Western Reserve University shows the superiority of the ResNet34-TL model compared with other classification models.

Deep learning-based fault diagnosis usually requires a rich supply of data, but fault samples are scarce in practice, posing a considerable challenge for existing diagnosis approaches to achieve highly accurate fault detection in real applications. This paper proposes an imbalanced fault diagnosis of rotatory machinery that combines time-frequency feature oversampling (TFFO) with a convolutional neural network (CNN). First, the sliding segmentation sampling method is employed to primarily increase the number of fault samples in the form of one-dimensional signals. Immediately after, the signals are converted into two-dimensional time-frequency feature maps by continuous wavelet transform (CWT). Subsequently, the minority samples are expanded again using the synthetic minority oversampling technique (SMOTE) to realize TFFO. After such two-fold data expansion, a balanced data set is obtained and imported to an improved 2dCNN based on the LeNet-5 to implement fault diagnosis. In order to verify the proposed method, two experiments involving single and compound faults are conducted on locomotive wheel-set bearings and a gearbox, resulting in several datasets with different imbalanced degrees and various signal-to-noise ratios. The results demonstrate the advantages of the proposed method in terms of classification accuracy and stability as well as noise robustness in imbalanced fault diagnosis, and the fault classification accuracy is over 97%.

The photo response non-uniformity (PRNU) is used to connect an image to its source sensor. In this paper, researchers propose a PRNU anonymity method based on image segmentation to cut the relationship between the image and its source camera. According to the distribution rule of PRNU in the high and low frequency band of the image, the high and low frequency information of the part is also processed differently, which ensures the quality of the output image to a large extent. Experiments on the datasets show that the proposed method can preserve the biometric characteristics of the device while maintaining the anonymity of the device. Comparing with prior art, peak signal to noise ratio (PSNR) and cosine similarity are improved by 1.9 dB and 0.02 points, respectively.

Sugar gourd shaped CeF 3 :Tb 3 + has been successfully synthesized by polyol method. The phase, morphology and fluorescent properties of CeF 3 :Tb 3 + were characterized by X-ray diffraction (XRD), transmission electron microscopy (TEM), infrared spectroscopy (IR) and photoluminescence (PL) spectra, respectively. It can be seen that the volume changes of giethylene glycol (DEG) and H 2 O content did not affect the phase structure of CeF 3 :Tb 3 + . The emission spectrum of CeF 3 : Tb 3 + shows the characteristic emissions of Tb 3 + ion, indicating that an energy transfer from Ce 3 + to Tb 3 + occurs in CeF 3 :Tb 3 + , and the decay time of CeF 3 :Tb 3 + was not affected by the change of DEG content.