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

TL;DR

This paper introduces a physics-guided neural network framework to detect unknown operational scenarios in safety-critical human-in-loop systems, demonstrated on an artificial pancreas system.

Contribution

It presents a novel physics-guided process model and hybrid neural network approach for early detection of unknown-unknowns in human-in-plant systems.

Findings

Successfully detected insulin cartridge errors in artificial pancreas

Demonstrated early identification of safety hazards

Introduced a physics-based surrogate model for anomaly detection

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.