| I&T Solution |
Develop AI on Predictive Maintenance and Performance Optimization for Main Low Voltage Switchboard
(REF: S-1352) |
| 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 |
| Position | : | Chief Innovation Officer | | Tel | : | 25502330 | | Email | : |
nigel@xtrasensing.com | | Webpage | : | www.XTRAsensing.com |
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