Summary of Artificial Intelligence Approaches For Predictive Maintenance in the Steel Industry: a Survey, by Jakub Jakubowski et al.
Artificial Intelligence Approaches for Predictive Maintenance in the Steel Industry: A Survey
by Jakub Jakubowski, Natalia Wojak-Strzelecka, Rita P. Ribeiro, Sepideh Pashami, Szymon Bobek, Joao Gama, Grzegorz J Nalepa
First submitted to arxiv on: 21 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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 Predictive Maintenance (PdM) is a crucial aspect of Industry 4.0, enabling operational efficiency, minimizing downtime, and extending equipment lifespan. AI-based PdM methods utilize data from industrial sensors to perform various tasks, including those in the steel industry. This survey synthesizes current knowledge on AI-based PdM in the steel industry, focusing on trends, research gaps, and practical implications. The study analyzed 219 articles, identifying common approaches, AI methods, and data characteristics. Trends show increasing interest in deep learning applications. Challenges include implementing proposed methods in production environments, integrating them into maintenance plans, and enhancing research accessibility and reproducibility. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Predictive Maintenance is a way to keep equipment working well by predicting when it might break down. This helps factories like those in the steel industry stay efficient and avoid downtime. Researchers are using Artificial Intelligence (AI) to help make this prediction more accurate. In this study, scientists looked at 219 papers on AI-based Predictive Maintenance in the steel industry. They found that most research focuses on blast furnaces and hot rolling, using data from sensors. The big picture is that this technology has a lot of potential to help industries like steel improve their operations. |
Keywords
» Artificial intelligence » Deep learning
