Aiming at the shortcomings of dynamic risk analysis in large gasification units, a method of dynamic Bayesian network based risk analysis of gasifier feed system was proposed. The fault tree model was established by using the relevant data of failure forms of each unit of gasifier feeding system, and the fault tree model was transformed into a Bayesian network model. The bayesian network model was optimized by K⁃2 algorithm, and the influence of common cause failure on the system was processed by β factor method. Considering the influence of maintenance factors on the failure rate of the system at each moment, the accuracy and feasibility of the proposed method were verified by using the Bayesian network analysis software GeNIe and Monte Carlo. The results show that the dynamic Bayesian network has the advantages of accurate dynamic risk analysis and the ability to identify the weak links in the gasifier feed system risk analysis. According to the analysis results, a reasonable reliability allocation strategy can be obtained.

In order to obtain the dynamic change law of reliability of redundant repairable system under the influence of common cause failure, a new algorithm combining improved GO method with dynamic bayesian network was proposed. Firstly, the GO diagram model was established according to the schematic diagram and flow chart of the system. Then, the GO model was converted into a DBN by using the conversion mapping rules of each operator and signal flow to the DBN. Finally, the model was solved by using DBN mature software and algorithms. After verification and comparison, it can be seen that this method is not only able to obtain the dynamic curve of system reliability and availability changing with time, but also to treat the influence of maintenance factors and common cause failures on the analysis results. In additional, that this method with the powerful ability of reverse reasoning, is able to obtain the weak links quickly and provide reference the fault diagnosis for the system

In order to solve the dynamic failure problem of pulverized coal pressurized conveying system, a reliability evaluation method based on dynamic bayesian network is proposed in this paper.Firstly, based on the analysis of the structure and function of the pulverized coal pressurized conveying system, the fault tree model is constructed, and the reliability analysis model of the pulverized coal pressurized conveying system is established according to the rules of the trans⁃formation from the fault tree model to the dynamic bayesian network.Then, by introducing maintenance factors and time series, using the two⁃way reasoning ability of dynamic bayesian network, the weak links and the dynamic variation law of reliability of the pulverized coal conveying system are determined.The results show that the method can effectively describe the dynamic characteristics of the pulverized coal pressurized conveying system, and the analysis results are more in line with the actual working conditions.

The rectangular beams were widely used in architectural engineering, cracks would be appeared with the increase of service life and some human factors, and any cracks in it might cause hidden risks. The SIF was a physical quantity that reflected the elastic stress field of the crack tip, which was related to the size of the crack, the geometrical characteristic of the component and the load. Meanwhile, it was very important to find a simple method to solve the SIF for not only the fracture theory but also engineering application, as it was a key content of fracture mechanics. In previous studies, the shape of initial crack must be known to get SIF, but it was very difficult to be seen under the condition of test. This paper took the bending beam as an example, a new method to formulize the SIF for three kinds of cracks in rectangular beam under bending load was proposed by the basis of the conservation  J integral.

mine environment for visualization was studied used OpenGL and Visual C++. Put forward a bold application of particle system to simulate mine airflow direction. The research for mine operation of the engineering and technical personnel to provide convenient conditions, without the master professional knowledge can be applied to the technical ease of mine system changes of various operations. At the same can achieve the controllability and visualization of mine, and in the design process must advantage and improve the deficiencies, as the mine daily production provides assistant decision function.