Quantitative Identification Cracks of Heritage Rock Based on Digital Image Technology

Digital image processing technologies are used to extract and evaluate the cracks of heritage rock in this paper. Firstly, the image needs to go through a series of image preprocessing operations such as graying, enhancement, filtering and binaryzation to filter out a large part of the noise. Then, in order to achieve the requirements of accurately extracting the crack area, the image is again divided into the crack area and morphological filtering. After evaluation, the obtained fracture area can provide data support for the restoration and protection of heritage rock. In this paper, the cracks of heritage rock are extracted in three different locations.The results show that the three groups of rock fractures have different effects on the rocks, but they all need to be repaired to maintain the appearance of the heritage rock.

Research on the Method of Coke Optical Tissue Segmentation Based on Adaptive Clustering

The microstructure is the key factor for quality discriminate of coke. In view of the characteristics of coke optical tissue (COT), a segmentation method of coke microstructures based on adaptive clustering was proposed. According to the strategy of multiresolution, adaptive threshold binarization and morphological filtering were carried out on COT images with lower resolution. The contour of the COT body was detected through the relationship checking between contours in the binary image, and hence, COT pixels were picked out to cluster for tissue segmentation. In order to get the optimum segmentation for each tissue, an advanced K -means method with adaptive clustering centers was provided according to the Calinski-Harabasz score. Meanwhile, Euclidean distance was substituted with Mahalanobis distance between each pixel in HSV space to improve the accuracy. The experimental results show that compared with the traditional K -means algorithm, FCM algorithm, and Meanshift algorithm, the adaptive clustering algorithm proposed in this paper is more accurate in the segmentation of various tissue components in COT images, and the accuracy of tissue segmentation reaches 94.3500%.

Research on fault diagnosis of planetary gearbox based on Variable Multi-Scale Morphological Filtering and improved Symbol Dynamic Entropy

Under complex working conditions with noise interference, the fault feature of planetary gearbox is difficult to be extracted and the fault mode is difficult to be identified. To tackle this problem, the technologies of Variable Multi-scale Morphological Filtering (VMSMF) and Average Multi-scale Double Symbolic Dynamic Entropy (AMDSDE) are proposed in this paper. VMSMF selects Chebyshev Window as the structural element and automatically selects the optimal scale parameters according to the signal characteristics of the planetary gearbox, which improves the filtering accuracy and calculation efficiency. AMDSDE fully considers the correlation between various state modes. Once combined with relevant knowledge of Mathematical statistics, the algorithm can effectively reduce misjudgment. Firstly, the Turn Domain Resampling (TDR) is used to transform the time domain signal of variable speed into the angle domain signal that is not affected by speed change. Secondly, the proposed VMSMF is used to de-noise the vibration signal, and the fault signal with a high signal-to-noise ratio is obtained. Finally, AMDSDE is used to extract the entropy value of the fault signal and judge the fault type. The proposed technology is verified by four kinds of signals collected from the sun gear of the planetary gearbox under non-stationary working conditions.

A feature extraction method of ship-radiated noise based on mathematical morphological filtering

In order to extract feature of ship signal more effectively, we propose a new approach for mathematical morphological filtering based on the morphological features. Mathematical morphological filter is a new nonlinear filter, which can effectively extract the edge contour and shape characteristics. The stimulation signal is processed by mathematical morphological filtering of different structure elements, which confirms the effect of morphological filtering on suppressing noise and preserving the nonlinear characteristics. Using flat structure element, the measured ship-radiated noise signals are processed by average filter, and the filtered signals are analyzed on the frequency spectrum. Compared with other filters, the result shows that the mathematical morphological filtering can successfully extract the effective information from the ship-radiated noise signals.