| I&T Solution |
Develop AI on Predictive Maintenance and Performance Optimization for Main Low Voltage Switchboard
(REF : S-1352)
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| Trial Project |
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| Solution Feature |
- Real-time monitoring (Dashboard)
- Data preprocessing and storage
- AI-powered analytics
- Fault detection and diagnosis
- Reporting and record-keeping
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| Trial Application and Expected Outcome |
- Real-time monitoring - The system should provide real-time monitoring of the main low voltage switchboard using smart power analyzers and sensor technologies.
- Data preprocessing and storage (Pipeline and Warehouse) - The system should ensure that the collected data is clean, complete, and ready for analysis.
- AI-powered analytics (Training) - The system should incorporate machine learning algorithms that can analyze the collected data and identify potential faults and abnormalities (A anomaly/fault detection).
- Fault detection and diagnosis (Production) - The system should be able to detect and diagnose faults in the switchboard, such as high impedance on busbars or potential damage to load equipment.
- Reporting and record-keeping - The system should generate reports that summarize the performance of the switchboard, including any detected faults and recommended optimizations.
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| Additional Solution Information |
20230414_XS_PdM-Inno_Portal-LVSW.pdf
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| Info on I&T Solution Provider |
| Solution Provider | : | XTRA Sensing Limited |
| Address | : | Suite 1603, Chinachem Johnston Plaza, 178-186 Johnston Road, Wan Chai, Hong Kong |
| Contact Person | : | Nigel Ko |
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Position | : | Chief Innovation Officer
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Tel | : |
25502330
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Email | : |
nigel@xtrasensing.com
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Webpage | : |
www.XTRAsensing.com
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