Today, predicting maintenance for your machine in a timely manner is essential to avoid downtime and to better plan production. Many technical solutions for predictive maintenance already exist. However, there are still many technical challenges in this area that can be solved by academics and the high-tech industry.
Raymon van Dinter (Junior Software Engineer at Sioux Technologies Apeldoorn - and external PhD Candidate at Wageningen University), Bedir Tekinerdogan (Wageningen University) and Cagatay Catal (Qatar University) have taken another step in the right direction. With this systematic literature review, various studies on predictive maintenance using Digital Twins have been brought together to provide starting points for further research.
Abstract of the paper
- Context:
Predictive maintenance is a technique for creating a more sustainable, safe, and profitable industry. One of the key challenges for creating predictive maintenance systems is the lack of failure data, as the machine is frequently repaired before failure. Digital Twins provide a real-time representation of the physical machine and generate data, such as asset degradation, which the predictive maintenance algorithm can use.
- Objective:
This research aims to gather and synthesize the studies that focus on predictive maintenance using Digital Twins to pave the way for further research.
- Method:
A systematic literature review (SLR) using an active learning tool is conducted on published primary studies on predictive maintenance using Digital Twins, in which 42 primary studies have been analyzed.
- Results:
This SLR identifies several aspects of predictive maintenance using Digital Twins, including the objectives, application domains, Digital Twin platforms, Digital Twin representation types, approaches, abstraction levels, design patterns, communication protocols, twinning parameters, and challenges and solution directions. These results contribute to a Software Engineering approach for developing predictive maintenance using Digital Twins in academics and the industry.
- Conclusion:
This study is the first SLR in predictive maintenance using Digital Twins. We answer key questions for designing a successful predictive maintenance model leveraging Digital Twins. We found that to this day, computational burden, data variety, and complexity of models, assets, or components are the key challenges in designing these models.
