Application of Digital Twins in Condition-Based Maintenance

Abstract

Condition-based Maintenance (CBM) is a combination of fault diagnosis, fault prognosis, and maintenance decisions. The diagnostic results can be used to predict the remaining life and maintenance plans are made based on current state and future state. Digital Twins (DTs) allows CBM to be carried out in a more efficient way. It simulates the operation of the system and realizes real-time interaction with the system. For this, the CBM model can get more data from the DTs and display the results through DTs. Compared with the traditional CBM, DT-based CBM is more intelligent, and thus, DTs-based CBM is widely studied in recent years. This paper presents the changes that DTs brings to CBM and the focus is the changes that DTs brings to fault diagnosis, fault prognoses, and maintenance decisions. The work divides the changes into three aspects, that is, DTs provides a new CBM framework, DTs provides data for CBM modeling and DTs provides good visualization tools. The future direction of DTs for CBM is also discussed in this paper.