Towards learning digital twin: case study on an anisotropic non-ideal rotor system

TL;DR

This paper introduces the concept of a learning digital twin (LDT) that adapts over time to physical degradation, demonstrated through a case study on an anisotropic rotor system, aiming to improve manufacturing accuracy.

Contribution

It proposes the novel concept of LDT focusing on lifelong learning and provides a detailed structure and algorithms, validated on a simulated rotor system degradation.

Findings

LDT effectively models the degradation process.

Algorithms enable accurate lifelong learning of the physical system.

Validation shows improved digital twin accuracy over time.

Abstract

In the manufacturing industry, the digital twin (DT) is becoming a central topic. It has the potential to enhance the efficiency of manufacturing machines and reduce the frequency of errors. In order to fulfill its purpose, a DT must be an exact enough replica of its corresponding physical object. Nevertheless, the physical object endures a lifelong process of degradation. As a result, the digital twin must be modified accordingly in order to satisfy the accuracy requirement. This article introduces the novel concept of "learning digital twin (LDT)," which concentrates on the temporal behavior of the physical object and highlights the digital twin's capacity for lifelong learning. The structure of a LDT is first described. Then, in-depth descriptions of various algorithms for implementing each component of a LDT are provided. The proposed LDT is validated on the simulated degradation process of an anisotropic non-ideal rotor system.