A supervised AI-based toolchain for anomaly detection, diagnosis, and reconfiguration for the life-support system of the COLUMBUS module of the ISS

This paper focuses on the development and implementation of a diagnosis toolchain, to identify faults and recommend actions for the system operators of the environmental control and life support system of the COLUMBUS module on the International Space Station. We present a comprehensive framework which uses different aspects of artificial intelligence to efficiently identify the necessary interventions for the system operator to stabilize the system in case of emergencies and defects. Methods such as machine learning and statistical analysis, based on time-series, are used for anomaly detection to identify potentially critical situations early and issue the corresponding warnings. Diagnostic functionality enables the identification of the causes of anomalies, integrating expert knowledge and pattern recognition algorithms to achieve accurate diagnostic results. The localization of affected system parts is crucial as fault propagation can obscure the origin of anomalies. A vital aspect of the AI system is determining possible reconfiguration measures according to the behavior of the system, offering operators various operational continuance variants in the event of damage. Based on the diagnostic results, the system identifies suitable reconfiguration measures to restore normal operation or minimize potential damage. An additional supervision module based on qualitative system models is then used to monitor, evaluate, and assess the effects of these interventions. An MLOps platform facilitates the seamless integration of the framework into existing processes, providing an agile solution for fast and reliable development, scaling, and standardized integration interfaces. The successful integration of the AI toolchain at Airbus Defense and Space exemplifies this implementation’s effectiveness, significantly reducing development times and enhancing the process’s reliability and efficiency.

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