Year: 2023
Type: conference-paper
Abstract: 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.
Authors: Shota Iino, Hideki Nomoto, Takashi Fukui, Sayaka Ishizawa, Miki Sasaki, Yohei Yagisawa, Takayuki Hirose, Yasutaka Michiura, Hiroharu Shibayama
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4388599237