Case-Based Reasoning for System Anomaly Detection and Management

Integrated System Health Management (ISHM) technologies are mission-critical for deep space exploration. Space habitats are complex systems, made up of several subsystems such as Life Support, Communications, Data Acquisition, Thermal Control, Environmental Monitoring, and Electrical Power Systems. Deep space missions will face increased latency in communications with Earth. As such, it is critical that deep space aircrafts and habitats be capable of detecting anomalies early enough to mitigate the effects of communication delays. It would also be beneficial if the space crew had the tools to not just detect anomalies but also to help them resolve them without waiting for assistance from ground control. Traditionally, model-based reasoning techniques have been effective in monitoring the health of such operations, but the rising demand for rapid fault detection and response in deep-space habitats calls for autonomous monitoring software that is agile, scalable, and can respond to previously unseen events. Data-driven approaches using machine learning techniques can provide the ability to detect novel anomalies and to evolve over time. However, they are not as effective at generating explanations or tracing root causes as rule-based modeling systems. In this paper, we describe a case-based reasoning (CBR) approach to developing ISHM tool that combines the data-driven approach with a method for diagnosing and explaining them. CBR is an artificial intelligence (AI) technique that aims to solve problems by analogy. The system, Anomaly Detection via Topological feature Maps (ADTM), uses self-organizing maps (SOMs) as an unsupervised learning approach for modeling each individual case. ADTM's case-base models represent knowledge about different operational modes, including faults, as individual cases. The design of appropriate indexing mechanisms is crucial to the effectiveness of a case-based model. We describe two different approaches to case-base indexing and retrieval and compare their performance on a data set from a simulation of a CubeSat.