Generation of knowledge base for Space Acceleration Measurement System (SAMS) data using an adaptive resonance theory 2-A (ART2-A) neural network

Abstract

Events aboard the space shuttle such as crew movement, crew exercise, thruster firings, etc., disrupt the microgravity environment required for many on-board experiments. Automatic detection of these events would allow astronauts to minimize their impact on experiments. Hence, using Space Acceleration Measurement System (SAMS) data collected on the USMP-3 mission, a knowledge base is generated to aid in the detection of disruptive events aboard the USMP-4 mission. Input patterns containing power spectral density (PSD) information of SAMS data are used to train an Adaptive Resonance Theory 2-A (ART2- A) neural network. The ART2-A neural network has been chosen because it has the ability to automatically add clusters as new input patterns are presented. The weight vectors of the ART2-A are used as the knowledge base. Using characteristic frequencies and acceleration magnitudes determined by Principal Investigator Microgravity Services (PIMS), each weight vector is assigned a label or name representing a set of events. The labeled knowledge base is then tested by presenting input patterns created from data collected during an exercise event.