STOTTLER HENKE ASSOCIATES, INC — National Aeronautics and Space Administration SBIR Phase I: H6

Description

<p style="margin-left:0in; margin-right:0in">We propose a machine-learning technology that significantly expands NASA&rsquo;s real-time and offline ISHM capabilities for future deep-space exploration efforts. Our proposed system, Anomaly Detection via Topological fEAture Map (AD-TEAM), will leverage a Self-Organizing Map (SOM)-based architecture to produce high-resolution clusters of nominal system behavior. What distinguishes AD-TEAM from more common clustering techniques (e.g., k-means) in the ISHM-space is that it maps high-dimensional input vectors to a 2D grid while preserving the topology of the original dataset. The result is a &lsquo;semantic map&rsquo; that serves as a powerful visualization tool for uncovering latent relationships between features of the incoming points. Thus, beyond detecting known and unknown anomalies, AD-TEAM will also enable space crew to semantically characterize the clusters discovered. In doing so, personnel will better understand how faults propagate throughout a system, the transitional states of subsystem degradation over time, and the dominant features (and their relationships) of subsystem behavior. In addition to analyzing single subsystem datasets, we also propose to cross-correlate subsystems in order to capture the cascading effect of faults from one subsystem to another, as well as discover latent relationships between subsystems. &nbsp;Such analysis would significantly aid in the maintenance and overhauling activities of NASA&rsquo;s deep-space missions.</p>