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In coal mining industry, equipment safety and reliability during operation are essential in the production process. In recent years, along with the continuous improvement of intelligent coal mining equipment, reliable production requirements have simultaneously increased. Therefore, this article aims to study the maintenance decision making method that considers the safety inspection of a fully mechanized mining equipment group. The goal is to establish a maintenance decision making model that considers the safety overhaul of fully mechanized mining equipment group in order to minimize the total maintenance, production costs and safety risks. Firstly, the regular safety inspection procedure and the maintenance process were introduced. Then, a model guideline method that considers a fully mechanized mining equipment group's safety inspection was established. Finally, through the comparison of algorithms and maintenance strategies, it is proved that the maintenance decision optimization method proposed in this paper is of great significance for improving equipment reliability, improving comprehensive operation rate, reducing maintenance costs, and ensuring coal mine safety production. [ABSTRACT FROM AUTHOR]

Nylon is a common engineering plastic that is easy to machine and can serve as a mechanical end-part. Ultrasonic testing is a highly efficient nondestructive testing method for the inspection of nylon materials. However, the curvature of the surface causes scattering or focusing of the ultrasonic beam bring difficulties to detect the inner defects. In addition, some configuration parameters, such as the water path depth, also affect the acoustic field distribution. It is necessary to predict the acoustic field distribution to achieve a better testing accuracy. An acoustic field simulation model based on a multi-Gaussian beam is established to predict the focused acoustic field in a multilayer medium under a curved interface. The relationship between the acoustic focusing area and the water path depth was explored, and the water path depth is selected by simulating the effective detection region. C-scan imaging of specimens with artificial flat-bottom holes are performed at different water path depths. By comparing the quantitative analysis results of different water path depths, the error of the FBH size obtained by selecting a water path depth is significantly less than others. The ultrasonic C-scan images of nylon bar with natural flaws are compared with the microscopic CT tomography reconstruction images, and the comparison shows that the defects can be detected in the C-scan images. The results show that the proposed method can optimize the configuration parameters to improve the accuracy of the flaw sizing. This study provides an efficient technique for the detection of nylon bars. [ABSTRACT FROM AUTHOR]