Summary of Revolutionizing System Reliability: the Role Of Ai in Predictive Maintenance Strategies, by Michael Bidollahkhani et al.
Revolutionizing System Reliability: The Role of AI in Predictive Maintenance Strategies
by Michael Bidollahkhani, Julian M. Kunkel
First submitted to arxiv on: 20 Apr 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Performance (cs.PF); 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 A comprehensive survey on predictive maintenance (Pd.M.) in computing continuum systems is presented, focusing on the integration of scalable artificial intelligence (AI) technologies. The study recognizes limitations of traditional maintenance practices and explores how machine learning and neural networks can enhance Pd.M. strategies. A thorough review of existing literature highlights key advancements, methodologies, and case studies. AI-driven predictive maintenance is examined for improving prediction accuracy and optimizing maintenance schedules, reducing downtime and enhancing system longevity. The survey concludes that AI-driven Pd.M. is instrumental in understanding the current landscape and future directions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Predictive maintenance in computing systems is getting smarter thanks to artificial intelligence. This paper looks at how different AI technologies like machine learning and neural networks can help predict when things might break down and plan maintenance accordingly. The study reviews what’s been done so far and finds that AI-driven predictive maintenance can make systems more reliable and cost-effective. |
Keywords
» Artificial intelligence » Machine learning
