Digital twin for predicting remaining useful life: a bibliometric and systematic literature review
Seema Rajkumar Nagrani, Vaibhav S. NarwanePurpose
Maintenance is essential in manufacturing industries to enhance productivity. Therefore, scheduling maintenance is crucial, which can be effectively achieved by predicting the remaining useful life (RUL) of equipment or systems.
Design/methodology/approach
The digital twin (DT) is a new paradigm for predicting RUL. Consequently, the SCOPUS database was utilized to investigate the role of digital twins in predicting RUL. Initially, 246 research documents (papers) were considered for the bibliometric literature review. Furthermore, a systematic literature review was conducted based on 63 research documents.
Findings
The use of DT for RUL prediction increased significantly, and multiple organizations were involved. China has published many research documents on DT for RUL. It was also noticeable that various simulation models were developed for RUL prediction using different tools and technologies.
Research limitations/implications
This study considers only the SCOPUS dataset. However, enough documents were discussed to get fruitful insights.
Practical implications
This study helps researchers, academics and industry practitioners implement DT to predict RUL.
Originality/value
This is one of the few studies that present a systematic literature review on RUL prediction using DT technology along with a conceptual framework.