Vibration modal shapes and strain measurement of the main shaft assembly of a friction hoist

In order to evaluate the reliability of the main shaft unit of a friction hoisting system, strain measurement is a significant method. In this paper, a test rig of a friction hoisting system was built, which could applied periodically changing load on its main shaft unit; The mechanical analysis under the test load was conducted and the boundary limits were obtained; A three dimensional model of the main shaft unit was built in Pro-E and its finite element analysis was performed in ANSYS; With the analytical result, measuring points for strain rosettes were initially selected; Vibration modal shapes of the main shaft unit were analyzed, based on which Modal Assurance Criterion (MAC) was utilized in the Particle Swarm Optimization (PSO) algorithm to make the final decision of the number and positions of the measuring points; A wireless measurement system was developed to acquire strain signals from the optimized measuring positions; The test result verified the efficiency of the methods employed in this paper and revealed how strain of the main shaft unit changes during running process

Research on denoising method of remote sensing image in mining area

Denoising is an important preprocessing step for the effective application of remote sensing images in mining area. The existing remote sensing image denoising methods based on statistics, domain transformation and learning generally have the problems of excessive smoothing of details and insufficient texture preservation. Based on the good edge-preserving property of guided filtering, an iterative guided filtering method is proposed. The method enhances the edge characteristics extraction effect of remote sensing images by guided mapping of residual information, and iteratively performing guided filtering and hyper-parameter shrinkage. The iterative guided filtering is combined with traditional wavelet soft threshold, non-local mean (NLM) filtering, block matching 3D(BM3D) filtering and other denoising methods, which improves the peak signal-to-noise ratio of the traditional method effectively. Among them, NLM filtering and BM3D filtering have the most obvious effects on improving the denoising performance. The iterative guided filtering and BM3D filtering are fused, and the denoised images are initially obtained through BM3D filtering to obtain residual data. The iterative guided filtering is used to process the residual data. While improving the image denoising effect, the image detail characteristics are well preserved. The iterative guided filtering and BM3D filtering fusion method are used for coal gangue yard identification and landslide area edge recognition in remote sensing images of mining areas, and good results have been achieved

Multiscale Time-Frequency Sparse Transformer Based on Partly Interpretable Method for Bearing Fault Diagnosis

Transformer model is being gradually studied and applied in bearing fault diagnosis tasks, which can overcome the feature extraction defects caused by long-term dependencies in convolution neural network (CNN) and recurrent neural network (RNN). To optimize the structure of existing transformer-like methods and improve the diagnostic accuracy, we proposed a novel method based on the multiscale time-frequency sparse transformer (MTFST) in this paper. First, a novel tokenizer based on shot-time Fourier transform (STFT) is designed, which processes the 1D format raw signals into 2D format discrete time-frequency sequences in the embedding space. Second, a sparse self-attention mechanism is designed to eliminate the feature mapping defect in naive self-attention mechanism. Then, the novel encoder-decoder structure is presented, the multiple encoders are employed to extract the hidden feature of different time-frequency sequences obtained by STFT with different window widths, and the decoder is used to remap the deep information and connect to the classifier for discriminating fault types. The proposed method is tested in the XJTU-SY bearing dataset and self-made experiment rig dataset, and the following work is conducted. The influences of hyperparameters on diagnosis accuracy and number of parameters are analysed in detail. The weights of the attention mechanism (AM) are visualized and analysed to study the interpretability, which explains the partly working pattern of the network. In the comparison test with other existing CNN, RNN, and transformer models, the diagnosis accuracy of different methods is statistically analysed, feature vectors are presented via the t-distributed stochastic neighbor embedding (t-SNE) method, and the proposed MTFST obtains the best accuracy and feature distribution form. The results demonstrate the effectiveness and superiority of the proposed method in bearing fault diagnosis