Systemic symptom detection in telemetry of ISS with explainability using FRAM and SpecTRM



Published Sep 4, 2023
Shota Iino Hideki Nomoto Takashi Fukui Sayaka Ishizawa Miki Sasaki Yohei Yagisawa Takayuki Hirose Yasutaka Michiura Hiroharu Shibayama


Explainability is important for machine learning-based anomaly detection of safety critical systems. In this respect, we propose a new systemic symptom detection method by combining two methodologies: the Functional Resonance Analysis Method (FRAM) and the Specification Tools and Requirement Methodology-Requirement Language (SpecTRM-RL) with machine learning-based normal behavior prediction model. The method was verified with data of thermal control system of Japanese Experimental Module of the International Space Station, and the result found that the proposed method enables flight controllers and specialists to obtain additional information for identifying causes of anomaly with the method.  

Abstract 129 | PDF Downloads 124



anomaly detection, aerospace, explainability, safety, formal method, machine learning

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