Detection of Unknown-Unknowns in Cyber-Physical Systems using Statistical Conformance with Physics Guided Process Models
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Unknown unknowns are operational scenarios in a cyber-physical system that are not accounted for in the design and test phase. As such under unknown-unknown scenarios, the operational behavior of the CPS is not guaranteed to meet requirements such as safety and efficacy specified using Signal Temporal Logic (STL) on the output trajectories. We propose a novel framework for analyzing the stochastic conformance of operational output characteristics of safety-critical cyber-physical systems that can discover unknown-unknown scenarios and evaluate potential safety hazards. We propose dynamics-induced hybrid recurrent neural networks (DiH-RNN) to mine a physics-guided surrogate model (PGSM) which is used to check the model conformance using STL on the model coefficients. We demonstrate the detection of operational changes in an Artificial Pancreas(AP) due to unknown insulin cartridge errors.
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