Data-driven Discovery of Cyber-Physical Systems

TL;DR

This paper introduces a data-driven framework for reverse engineering cyber-physical systems, enabling modeling and prediction of their behavior without relying on complex physical models.

Contribution

It presents a novel general framework for reverse engineering CPSs directly from data, including physical system identification and transition logic inference.

Findings

Successfully applied to real-world mechanical, electrical, and medical systems

Enables prediction of CPS trajectories based on discovered models

Supports performance assessment and failure-proof design of CPS

Abstract

Cyber-physical systems (CPSs) embed software into the physical world. They appear in a wide range of applications such as smart grids, robotics, intelligent manufacture and medical monitoring. CPSs have proved resistant to modeling due to their intrinsic complexity arising from the combination of physical components and cyber components and the interaction between them. This study proposes a general framework for reverse engineering CPSs directly from data. The method involves the identification of physical systems as well as the inference of transition logic. It has been applied successfully to a number of real-world examples ranging from mechanical and electrical systems to medical applications. The novel framework seeks to enable researchers to make predictions concerning the trajectory of CPSs based on the discovered model. Such information has been proven essential for the assessment of the performance of CPS, the design of failure-proof CPS and the creation of design guidelines for new CPSs.