Valves play a critical role in rocket-engine test stands because they are essential for the cryogen transport mechanisms that are vital to test operations. Sensors that are placed on valves monitor the pressure, temperature, flow rate, valve position, and any other features that are required for diagnosing their functionality. Integrated system-health management (ISHM) algorithms have been used to identify and evaluate anomalous operating conditions of systems and subsystems (e.g., valves and valve components) on complex structures, such as rocket test stands. In order for such algorithms to be useful, there is a need to develop realistic models for the most common and problem-prone elements. Furthermore, the user needs to be provided with efficient tools to explore the nature of the anomaly and its possible effects on the element, as well as its relationship to the overall system state. This paper presents the development of an intelligent-valve framework that is capable of tracking and visualizing events of the large linear actuator valve (LLAV) in order to detect anomalous conditions. The framework employs a combination of technologies, including a dynamic data exchange data-transfer protocol, autoassociative neural networks, empirical and physical models, and virtual-reality environments. The diagnostic procedure that is developed has the ability to be integrated into existing ISHM systems and can be used for assessing the integrity of rocket-engine test-stand components.
|Original language||English (US)|
|Number of pages||9|
|Journal||IEEE Transactions on Instrumentation and Measurement|
|State||Published - Apr 2011|
All Science Journal Classification (ASJC) codes
- Electrical and Electronic Engineering