Anomaly Detection via Topological Feature Map, Phase I

Metadata Updated: November 12, 2020

We propose a machine-learning technology that significantly expands NASA’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 ‘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, 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.  Such analysis would significantly aid in the maintenance and overhauling activities of NASA’s deep-space missions.

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Public: This dataset is intended for public access and use. License: No license information was provided. If this work was prepared by an officer or employee of the United States government as part of that person's official duties it is considered a U.S. Government Work.

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Metadata Created Date November 12, 2020
Metadata Updated Date November 12, 2020

Metadata Source

Harvested from NASA Data.json

Additional Metadata

Resource Type Dataset
Metadata Created Date November 12, 2020
Metadata Updated Date November 12, 2020
Publisher Space Technology Mission Directorate
Unique Identifier Unknown
Identifier TECHPORT_94494
Data First Published 2019-01-01
Data Last Modified 2020-01-29
Public Access Level public
Bureau Code 026:00
Metadata Context
Metadata Catalog ID
Schema Version
Catalog Describedby
Homepage URL
Program Code 026:027
Source Datajson Identifier True
Source Hash a921d5eae6b37a80b3940fc9339bb9e31d4e1c70
Source Schema Version 1.1

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