Assessment Method Research Of Health Condition For Liquid Rocket Engine Test Facilities

With increasing demand of aerospace industry and the development of technology, rocket technology is of great importance in space launch. The most important part of the rocket is rocket engine which limits the advance of rocket technology. Due to the vast consumption of human resources, material resources, financial recourses of the launch, a launch failure will result in catastrophic consequence and huge economic losses. To ensure the reliability, efficiency, economy in the process of developing rocket engines, extensive ground tests should be carried out. For the large scale, complex structure, part variety, multiple influential factors of the rocket engine test platform, it includes sensors, communication systems, telemetry systems, control systems etc. The stabilization and reliability of the rocket test platform is the key point in the development of the rocket.In order to obtain an intuitive understanding of the rocket test platform, the status of platform could be categorized in normal operation, slight fault and major failure based on the type, location, characteristic of the fault and the impact on the health. In this paper, the research provides a variety of algorithms to evaluate the status of sensors and test platform. The research focuses on the application of the gray comprehensive evaluation method, the information entropy, the fuzzy comprehensive evaluation in health assessment. The effectiveness is verified by experimental data.The gray comprehensive evaluation is utilized to analyze the relationship information in the data, i.e., by calculating gray relational degree to determine the similarity and diversity of data. By calculated the similarity between the obtained data and optimal index, the health status of the platform can be evaluated.The information entropy introduces the concept of entropy in the thermodynamic chemistry to information theory. The operation status can be obtained by variation intensity of the information. The larger the information entropy, indicating more complex information changes, the more unstable the system is; and the smaller the information entropy, indicating less change of information, the more the system tends to function properly.The main idea of the health degree method is: First, the appropriate fuzzy membership function is determined based on historical data variation. Then, the membership of current system is calculated. Finally, with the corresponding weight, the overall health degree is obtained to to evaluate the status of the system.By analyzing the numerical simulation results of three methods, the characteristics of the methods are obtained. The efficiency and effectiveness are validated. To select the appropriate one of the methods, or a combination of the methods can evaluate the health status of the test platform.