Moddemann, Lukas

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.

Fault diagnosis algorithms compute faulty components by comparing actual observations against some model of known behaviour. A major challenge for fault diagnosis lies in creating such a suitable model. In the past, models were usually assumed to be given by experts. But in modern cyber-physical systems this assumption cannot be held, as experts are expensive and system architectures may be subject to change. This article presents a novel algorithm to obtain those models automatically and apply them for fault diagnosis. The evaluation was done on the Tennessee Eastman process and on two benchmarks of multiple-tank systems.

Kubeflow integrates a suite of powerful tools for Machine Learning (ML) software development and deployment, typically showcased independently. In this study, we integrate these tools within an end- to-end workflow, a perspective not extensively explored previously. Our case study on anomaly detection using telemetry data from the International Space Station (ISS) investigates the integration of various tools—Dask, Katib, PyTorch Operator, and KServe—into a single Kubeflow Pipelines (KFP) workflow. This investigation reveals both the strengths and limitations of such integration in a real-world context. The insights gained from our study provide a comprehensive blueprint for practitioners and contribute valuable feedback for the open source community developing Kubeflow.