Date of Award
5-2020
Document Type
Thesis
Publisher
Santa Clara : Santa Clara University, 2020.
Degree Name
Master of Science (MS)
Department
Computer Science and Engineering
First Advisor
Christopher Kitts
Abstract
Anomaly management—the detection, diagnosis, and resolution of anomalies in a system—is traditionally performed using experiential techniques which are quickly computed, but poorly structured. Newer model-based approaches are more systematic and higher performing but are computationally expensive, which is a particular challenge for execution in an operational environment. This paper builds on a novel system to pre-compute model-based anomaly symptoms to enable quick retrieval and diagnosis in operational settings. New additions to this system include a simplified model interface, anomaly likelihoods associated with each component, and easier interpretation of results. The implemented system has been used successfully to detect and diagnose anomalies in a baseline test circuit as well as in an operational satellite monitoring network. Results show that this approach is promising; with a thorough model, the diagnosis and resolution processes of anomaly management could be greatly improved for more complex remote systems such as university-operated nanosatellites and field robotic vehicles.
Recommended Citation
Hedlund, Jake, "Prioritized Anomaly Catalog Generation Using Model-Based Reasoning" (2020). Computer Science and Engineering Master's Theses. 16.
https://scholarcommons.scu.edu/cseng_mstr/16