Learning faults in time: sequential behavioural modelling for complex fault detection in multi-robot systems

Reliable fault detection in multi-robot systems requires models capable of capturing complex, time-dependent fault signatures that manifest over extended temporal horizons rather than instantaneous observations alone. Existing data-driven approaches operate reactively on behavioural snapshots, failing to capture fault modes whose discriminative signature depends on temporally ordered precursors. This work formalises a theoretical impossibility result demonstrating that memoryless classifiers are

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ethics.ai (29 July 2026), “Learning faults in time: sequential behavioural modelling for complex fault detection in multi-robot systems,” evidence record 14230, https://ethics.ai/record/14230 (originally published by Frontiers in Robotics and AI).

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