Abstract
The aim of this paper is to contribute to machine-learning technology that expands real-time and offline Integrated System Health Management capabilities for future deep-space exploration efforts. To this end, we have developed Anomaly Detection via Topological feature-Map (ADTM), which leverages a Self-Organizing Map (SOM)-based architecture to produce high-resolution clusters of nominal system behavior. What distinguishes ADTM from more common clustering techniques (e.g. k-means) 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 tool for uncovering latent relationships between features of the incoming data points. We successfully modeled and analyzed datasets from a NASA Ames Research Center Graywater Recycling System which documents a real hardware system fault. Our results show that ADTM effectively detects both known and unknown anomalies and identifies the correlated measurands from models trained using just nominal data.
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Rosengarten, M., Ramachandran, S. (2020). SOM-Based Anomaly Detection and Localization for Space Subsystems. In: Vellido, A., Gibert, K., Angulo, C., Martín Guerrero, J. (eds) Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization. WSOM 2019. Advances in Intelligent Systems and Computing, vol 976. Springer, Cham. https://doi.org/10.1007/978-3-030-19642-4_6
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