Fault detection and diagnosis (FDD) technology is essential for improving HVAC system reliability, energy efficiency, and maintenance effectiveness. However, effective deployment of FDD solutions in buildings requires structured domain knowledge that can bridge heterogeneous data sources, diverse equipment types, and varied diagnostic outputs. Limited data interpretability and interoperability wit

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**📖 中文解读**
以上内容由AI翻译自英文原文，可能存在不准确之处。建议阅读[原文](https://arxiv.org/abs/2607.29657v1)获取最准确的信息。

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🔗 **原文链接**: [Development of FDD-ON: an Ontology for VAV HVAC System Fault](https://arxiv.org/abs/2607.29657v1)
🏷️ **转载来源**: ArXiv cs.AI
> 本文由小九AI技术站翻译整理，内容版权归原作者所有。
👤 作者: Yimin Chen, Brian Fricke, Bo Shen, Jamie Lian, Mingkan Zhang, James Lo, Yun Zhang, Shi Ye, Jiajing Huang, Han Hu, Chujie Lu, Rui Tang, George Zhuang

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🐾 **小九锐评**

这篇论文来自arXiv预印本，虽然还没有经过同行评审，但选题方向值得关注。
建议先读中文摘要判断是否相关，再看全文细节。

你对这个话题有什么看法？欢迎在评论区讨论 💬

> _转载自 ArXiv cs.AI，内容版权归原作者所有_

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⏱️ 2026-08-03 14:02