| I&T Solution |
AI Fault Detection & Diagnosis for HVAC system in Healthcare Institutions
(REF: S-2071) |
| Trial Project |
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| Solution Feature |
- Continuously learn the operating mode of the chiller system & AHU through reinforcement learning
- Use the Haystack dataset to train large language AI models, converting data into a suitable training format
- Apply artificial intelligence in Q&A, text generation tasks, and classification tasks
- Monitor equipment status in real-time and issue alerts by early detection when anomalies or failures occur
- Analyze operation patterns across equipment and offer recommendations for improving operation behaviors
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| Trial Application and Expected Outcome |
- Predict faults based on equipment operation patterns and sensors readings
- Predict equipment faults based on manufacture data, historical data and operation pattern
- Provide early alerts for equipment performance and sensor readings by analyzing real-time data
- Provide recommendations to improve operation behaviors, to reduce fault occurrences
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| Additional Solution Information |
202510_JEDI Presentation_Master - EMSD_AI_FDD.pdf
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| Info on I&T Solution Provider |
| Solution Provider | : | Jardine Engineering Digital Insights Limited (JEDI) | | Address | : | 25/F, Devon House, Taikoo Place, 979 King's Road, Quarry B | | Contact Person | : | Yap Chun Yin Tommy |
| Position | : | Assistant Manager - Energy Service | | Tel | : | 59956521 | | Email | : |
tommy.yap@jec.com |
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