We are developing a machine-learning technology that significantly expands NASA’s real-time and offline ISHM capabilities for future deep-space exploration efforts. Our system, Anomaly Detection via Topological feature Map (AD-TEAM), is leveraging 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 ‘semantic map’ 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, AD-TEAM will 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.