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Machine Learning-based on board autonomy, failure prognostics and detection

Programme Reference
Prime Contractor
Airbus Defence and Space GmbH
Start Date
End Date
The objective of this activity is to design, test and prototype a generic and reusable deep learning approach for both anomaly detection amp; isolation as well as failure prognostics.
A critical function on board all spacecraft is the Failure Detection, Isolation and Recovery (FDIR) subsystem, which is vital in ensuring the safety, autonomy and availability of the system. Modern satellites complexity is increasing, and these highly complex systems require bespoke FDIR solutions with complicated architectures and difficult testability.
Furthermore, traditional FDIR techniques are generally good at detecting 'single' failures but limited in isolation capabilities, and struggling when multiple faults combine in non-foreseen behaviours. The resulting functional unavailability and operational costs can be prohibitive ? for example ? for large fleets or constellation.
The solutions developed in this activity offer the benefit of moving from a constant data downstream of all parameters (data-pull) with threshold-based alerting to a predictive alerting system that can handle seasonal effects, system noise and data correlations (information-push) scalable to very large fleets of satellites. These functionalities can be migrated from ground on-board the spacecraft, as the algorithms are very lean, require only little computational power and are highly energy-efficient.
This activity aims at demonstrating that a highly reusable, generic FDIR solution can perform satisfactorily in flight, streamlining the current FDIR development process in both time and cost whilst increasing the autonomy and availability of the next generation of spacecraft. Furthermore, it offers serious contributions towards raising the TRL of neural network implementations on space-qualified HW to flight SW standards, setting a precedent for future innovative machine learning-based applications in on-board functional avionics.
By implementing embedded-AI-based analysis of on board TM time series, the failure detection results can be applied not only to trigger alarms when a fault occurs, but also for preventive maintenance. This applies both to stand alone electronics (e.g. high performance COTS FPGA, ?C) to generic units, assemblies, systems.
The key feature of the proposed activity is the real-time condition monitoring of all spacecraft parameters. The algorithms learn from the machine itself by training on-ground during simulation/testing (e.g. hardware-in-the-loop) of the spacecraft to identify patterns in the data that characterize nominal operations. The observed patterns are used as a reference when compared to instantaneous data. In case unusual patterns are detected, the operator is informed about identified deviations, significant statistics and correlated patterns for further analysis. A deviation manifests earlier than the fault itself. Thus, appropriate actions can be taken not only to recover from faults, but also to prevent faults from occurring or to reduce their impact.
Main tasks:
    • Selection of target spacecraft(s) + use case elaboration;
    • Development of Machine learning based FDIR algorithms with both unit/system test and qualification data and in flight data;
    • Verification of the algorithms performances on suitable on-board processors that will enable running deep learning applications on the edge;
    • Dry run on 'ground data' (coming from unit/system level tests) and experimentation on in-flight data on past missions; ;
    • Preparation for suitable In Orbit Demonstration for upcoming missions.;
Application Domain
Generic Technologies
Technology Domain
1 - On-board Data Subsystems
2 - Space System Software
4 - Space Systems Environments and Effects
5 - Space System Control
8 - System Design & Verification
Competence Domain
3-Avionic Systems
49-Artificial Intelligence
Initial TRL
Target TRL
Achieved TRL