Summary of A Maturity Framework For Data Driven Maintenance, by Chris Rijsdijk et al.
A maturity framework for data driven maintenance
by Chris Rijsdijk, Mike van de Wijnckel, Tiedo Tinga
First submitted to arxiv on: 26 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG); Systems and Control (eess.SY)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 paper explores the challenges in data-driven maintenance and proposes a framework to assess the maturity of maintenance decisions. The framework considers four aspects: data/decision maturity, translation from real-world to data, computability of decisions using models, and causality in obtained relations. The paper discusses theoretical concepts and applies the framework to a practical fault detection and identification problem, comparing two approaches: experience-based and model-based. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how machines can make their own maintenance decisions by using data. It proposes a way to measure how mature these decisions are. The approach considers four things: what kind of data is used, how well the data translates into a decision, whether a computer can make the decision, and why something is happening (causality). The paper talks about some big ideas behind this and then uses it to look at a real problem where machines have to detect and fix faults. Two different ways to do this are compared. |
Keywords
* Artificial intelligence * Translation
