Summary of Explainable Predictive Maintenance: a Survey Of Current Methods, Challenges and Opportunities, by Logan Cummins et al.
Explainable Predictive Maintenance: A Survey of Current Methods, Challenges and Opportunities
by Logan Cummins, Alex Sommers, Somayeh Bakhtiari Ramezani, Sudip Mittal, Joseph Jabour, Maria Seale, Shahram Rahimi
First submitted to arxiv on: 15 Jan 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Human-Computer Interaction (cs.HC); Machine Learning (cs.LG)
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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 This paper surveys the current state of Explainable Predictive Maintenance (XPM), an application of Explainable AI (XAI) to predictive maintenance techniques. By introducing transparency and interpretability into predictive systems, XPM aims to increase trust among human operators while maintaining optimal system performance. The authors categorize existing XPM methods into groups based on the XAI literature, discuss current challenges, and provide future research directions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Predictive maintenance uses AI and machine learning to prolong mechanical systems’ life by predicting when maintenance is needed. This helps reduce costs and downtime. But for critical applications, humans need to trust these predictions. That’s where Explainable AI comes in – it makes the predictive system more transparent and easier to understand. This paper looks at how XAI is used in predictive maintenance, grouping different methods together based on existing research. It also talks about challenges and what we might see in future research. |
Keywords
* Artificial intelligence * Machine learning
