Monitoring the Microgravity Environment Quality On-Board the International Space Station Using Soft Computing Techniques

This paper presents an artificial intelligence monitoring system developed by the NASA Glenn Principal Investigator Microgravity Services project to help the principal investigator teams identify the primary vibratory disturbance sources that are active, at any moment in time, on-board the International Space Station, which might impact the microgravity environment their experiments are exposed to. From the Principal Investigator Microgravity Services’ web site, the principal investigator teams can monitor via a graphical display, in near real time, which event (s) is /are on, such as crew activities, pumps, fans, centrifuges, compressor, crew exercise, platform structural modes, etc., and decide whether or not to run their experiments based on the acceleration environment associated with a specific event. This monitoring system is focused primarily on detecting the vibratory disturbance sources, but could be used as well to detect some of the transient disturbance sources, depending on the events duration. The system has built-in capability to detect both known and unknown vibratory disturbance sources. Several soft computing techniques such as Kohonen’s Self-Organizing Feature Map, Learning Vector Quantization, Back-Propagation Neural Networks, and Fuzzy Logic were used to design the system. INTRODUCTION With the International Space Station (ISS) soon to be operational, many of the scientific experiments, which used to be conducted on-board the NASA Space Shuttle orbiters for long duration microgravity conditions will be conducted onboard the ISS, which will allow even longer periods of microgravity. Many of these scientific experiments will require knowledge of the microgravity environment for accurate analysis of the experimental data. The Microgravity Measurement and Analysis Project (MMAP) at NASA Glenn Research Center (GRC), which the Principal Investigator Microgravity Services (PIMS) project is a part of, has the responsibility for measuring, analyzing, and characterizing the microgravity environment for Principal Investigator (PI) teams and providing expertise in microgravity environment assessment. The PIMS project at the NASA Glenn Research center supports NASA’s Microgravity Research Division Principal Investigators (PIs) by providing acceleration data analysis and interpretation for a variety of microgravity carriers such as the Space Shuttle, parabolic aircraft (KC-135), sounding rocket, drop towers, the Russian Mir Space Station, and the International Space Station (ISS). In general, the PIMS project’s acceleration data support efforts are to archive and disseminate accelerometer data; to support users interested in the microgravity acceleration environment by NASA/TM—2001-210943 1 NASA/TM—2001-210943 2 providing information about activities and acceleration sources; to identify acceleration sources related to vehicle systems, experiment hardware, vibration isolation systems, and other systems; to develop data analysis techniques and displays per user requirements; to educate users about the environment and data analysis techniques; to provide standard data interpretation reports; and to characterize the microgravity environment of the ISS in support of PIs. PIMS has characterized the microgravity environment for various earth orbiting platforms as well as ground based facilities in support of PIs from various science disciplines such as biotechnology, combustion science, fluid physics, materials science, and fundamental physics. With the advent of the ISS operation, a vast amount of acceleration data is expected to be collected, processed, and analyzed for both the ISS microgravity environment characterization (verification) and scientific experiments. This offers a unique challenge for the task of data analysis. A comprehensive means of examining the collected data to assist in identifying significant acceleration trends and events is needed. To tackle that problem, the NASA Glenn PIMS project is currently developing an artificial intelligence monitoring system, which will show the principal investigator teams in near real time any change in the microgravity environment that might affect their experiments. This Artificial Intelligence (AI) monitoring system will extract, analyze, and interpret the most salient features of the microgravity environment on-board the ISS at any moment, in near real time, as data is downlinked from the ISS for processing. Such a system will help the Principal Investigator (PI) teams monitor the microgravity environment on-board the ISS in order to avoid, whenever possible, any negative impact on their experiments. The PIMS ISS Microgravity Environment Monitoring System (MEMS) will do the following: 1) detect the current vibratory events on-board the ISS in near real time; 2) classify each known event and assess their relative impact on the environment; 3) identify unknown events, which require characterization. The system will act as the expert eyes for the PIs, thus freeing them from the burden of being a microgravity environment analyst expert so that they can concentrate on running / analyzing their experiments. It is important to note that the MEMS’ main focus is the vibratory regime, but some of the transient activities could be detected as well, depending on the events duration. MICROGRAVITY ENVIRONMENT The microgravity acceleration environment of an orbiting spacecraft in a low earth orbit is a very complex phenomenon. Many factors, such as experiment operation, life-support systems, equipment operation, crew activities, aerodynamic drag, gravity gradient, rotational effects as well as the vehicle structural resonance frequencies (structural modes) contribute to form the overall microgravity environment. The microgravity acceleration environment, in general, can be considered as made up of three components: quasi-steady, vibratory, and transient components.