Summary of State-of-the-art Review: the Use Of Digital Twins to Support Artificial Intelligence-guided Predictive Maintenance, by Sizhe Ma et al.
State-of-the-Art Review: The Use of Digital Twins to Support Artificial Intelligence-Guided Predictive Maintenance
by Sizhe Ma, Katherine A. Flanigan, Mario Bergés
First submitted to arxiv on: 19 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed AI-based predictive maintenance (PMx) framework integrates Digital Twins (DTs) to overcome current challenges and enable large-scale automation across various stakeholders. The paper highlights the potential of DTs in PMx, citing the importance of real-time automation, monitoring, analysis, and prediction tasks. However, it also notes that current DT applications have limitations, particularly with regards to information requirements (IRs) and functional requirements (FRs). To address these gaps, the authors provide a comprehensive roadmap for DT evolution, structured in three stages: referencing prior work on IRs and FRs, conducting a literature review of current DT applications, and outlining necessary components for a unified framework. The paper concludes with research directions aimed at seamlessly integrating DTs into PMx. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Predictive maintenance (PMx) uses artificial intelligence to make predictions about when equipment might break down or need maintenance. This can help reduce waste and save money. However, current methods have some problems, like being hard to understand and not using all the available data. A new approach called Digital Twins (DTs) could help fix these issues by allowing for more automation and better decision-making. The paper looks at how DTs are currently used in PMx and what limitations they face. It also provides a plan for improving DTs so that they can be used to automate PMx more effectively. |
