Detection of Unknown-Unknowns in Human-in-Plant Human-in-Loop Systems Using Physics Guided Process Models

• URL: http://arxiv.org/abs/2309.02603v2
• Date: Tue, 12 Dec 2023 17:49:46 GMT
• Title: Detection of Unknown-Unknowns in Human-in-Plant Human-in-Loop Systems Using Physics Guided Process Models
• Authors: Aranyak Maity, Ayan Banerjee and Sandeep Gupta
• Abstract summary: We propose a novel framework for analyzing the operational output characteristics of safety-critical HIL-HIP systems. We propose dynamics-induced hybrid recurrent neural networks (DiH-RNN) to mine a physics-guided surrogate model (PGSM) The PGSM enables early detection of unknown-unknowns based on the physical laws governing the system.
• Score: 4.702143872609881
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Unknown-unknowns are operational scenarios in systems that are not accounted for in the design and test phase. In such scenarios, the operational behavior of the Human-in-loop (HIL) Human-in-Plant (HIP) systems is not guaranteed to meet requirements such as safety and efficacy. We propose a novel framework for analyzing the operational output characteristics of safety-critical HIL-HIP 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) that checks for deviation of the cyber-physical system (CPS) from safety-certified operational characteristics. The PGSM enables early detection of unknown-unknowns based on the physical laws governing the system. We demonstrate the detection of operational changes in an Artificial Pancreas(AP) due to unknown insulin cartridge errors.

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