Towards Explainable Anomaly Detection in Safety-critical Systems: Employing FRAM and SpecTRM in International Space Station Telemetry
Ensuring the reliability and safety of space missions necessitates advanced anomaly detection systems capable of not only identifying deviations but also providing clear, understandable insights into their causes. This paper introduces a novel methodology for the detection of systemic anomalies in t...
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| Format: | Article |
| Language: | English |
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The Prognostics and Health Management Society
2024-10-01
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| Series: | International Journal of Prognostics and Health Management |
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| Online Access: | https://papers.phmsociety.org/index.php/ijphm/article/view/3857 |
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| author | Shota Iino Hideki Nomoto Takashi Fukui Sayaka Ishizawa Yohei Yagisawa Takayuki Hirose Yasutaka Michiura |
| author_facet | Shota Iino Hideki Nomoto Takashi Fukui Sayaka Ishizawa Yohei Yagisawa Takayuki Hirose Yasutaka Michiura |
| author_sort | Shota Iino |
| collection | DOAJ |
| description | Ensuring the reliability and safety of space missions necessitates advanced anomaly detection systems capable of not only identifying deviations but also providing clear, understandable insights into their causes. This paper introduces a novel methodology for the detection of systemic anomalies in the telemetry data of the International Space Station (ISS), leveraging the synergistic application of the Functional Resonance Analysis Method (FRAM) and the Specification Tools and Requirement Methodology- Requirement Language (SpecTRM-RL). Integrated with machine learning-based normal behavior prediction model, this approach significantly enhances the explanatory of anomaly detection mechanisms. The methodology is verified and validated through its application to the thermal control system within the ISS's Japanese Experimental Module (JEM), illustrating its capacity to augment diagnostic capabilities and assist flight controllers and specialists in preserving the ISS's functional integrity. The findings underscore the importance of explainability in the machine learning-based anomaly detection of safety-critical systems and suggest a promising avenue for future explorations aimed at bolstering space system health management through improved explainability and operational resilience. |
| format | Article |
| id | doaj-art-66a76ea0232e4808bb0d7d9cf32a56ab |
| institution | Kabale University |
| issn | 2153-2648 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | The Prognostics and Health Management Society |
| record_format | Article |
| series | International Journal of Prognostics and Health Management |
| spelling | doaj-art-66a76ea0232e4808bb0d7d9cf32a56ab2025-08-20T03:48:31ZengThe Prognostics and Health Management SocietyInternational Journal of Prognostics and Health Management2153-26482024-10-01153112https://doi.org/10.36001/ijphm.2024.v15i3.3857Towards Explainable Anomaly Detection in Safety-critical Systems: Employing FRAM and SpecTRM in International Space Station TelemetryShota Iino0Hideki Nomoto1Takashi Fukui2Sayaka Ishizawa3Yohei Yagisawa4Takayuki Hirose5Yasutaka Michiura6Japan Manned Space Systems CorporationJapan Manned Space Systems CorporationJAPAN NUS Co.JAPAN NUS Co.JAPAN NUS Co.Japan Manned Space Systems CorporationJapan Manned Space Systems CorporationEnsuring the reliability and safety of space missions necessitates advanced anomaly detection systems capable of not only identifying deviations but also providing clear, understandable insights into their causes. This paper introduces a novel methodology for the detection of systemic anomalies in the telemetry data of the International Space Station (ISS), leveraging the synergistic application of the Functional Resonance Analysis Method (FRAM) and the Specification Tools and Requirement Methodology- Requirement Language (SpecTRM-RL). Integrated with machine learning-based normal behavior prediction model, this approach significantly enhances the explanatory of anomaly detection mechanisms. The methodology is verified and validated through its application to the thermal control system within the ISS's Japanese Experimental Module (JEM), illustrating its capacity to augment diagnostic capabilities and assist flight controllers and specialists in preserving the ISS's functional integrity. The findings underscore the importance of explainability in the machine learning-based anomaly detection of safety-critical systems and suggest a promising avenue for future explorations aimed at bolstering space system health management through improved explainability and operational resilience.https://papers.phmsociety.org/index.php/ijphm/article/view/3857explainableinterpretableanomaly detectionaerospace |
| spellingShingle | Shota Iino Hideki Nomoto Takashi Fukui Sayaka Ishizawa Yohei Yagisawa Takayuki Hirose Yasutaka Michiura Towards Explainable Anomaly Detection in Safety-critical Systems: Employing FRAM and SpecTRM in International Space Station Telemetry International Journal of Prognostics and Health Management explainable interpretable anomaly detection aerospace |
| title | Towards Explainable Anomaly Detection in Safety-critical Systems: Employing FRAM and SpecTRM in International Space Station Telemetry |
| title_full | Towards Explainable Anomaly Detection in Safety-critical Systems: Employing FRAM and SpecTRM in International Space Station Telemetry |
| title_fullStr | Towards Explainable Anomaly Detection in Safety-critical Systems: Employing FRAM and SpecTRM in International Space Station Telemetry |
| title_full_unstemmed | Towards Explainable Anomaly Detection in Safety-critical Systems: Employing FRAM and SpecTRM in International Space Station Telemetry |
| title_short | Towards Explainable Anomaly Detection in Safety-critical Systems: Employing FRAM and SpecTRM in International Space Station Telemetry |
| title_sort | towards explainable anomaly detection in safety critical systems employing fram and spectrm in international space station telemetry |
| topic | explainable interpretable anomaly detection aerospace |
| url | https://papers.phmsociety.org/index.php/ijphm/article/view/3857 |
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