Fault Detection and Diagnostics (FDD) identifies abnormalities in the performance of equipment including chillers, motors, pumps, cooling towers, air handlers, ventilation fans, VAV boxes etc.
Carnot Innovations’s Logic based FDD along with Machine Learning based anomaly prediction algorithms provide actionable insights that enable operators to perform targeted defect rectification at optimal times.
The analytics engine translates irregularities and glitches into fault notifications which are delivered to operators by not only detailing the cause of the issue, but also providing recommendations to solve the identified defects and faults.
These diagnostic opportunities often reveal opportunities to improve control logic and operations, especially relating to equipment that are not actively monitored. Fault Detection is based on data collected from the device directly, providing live feedback of the facility.
Data was collected at 15 minute intervals from 100,000 sensor points located in all 10 buildings and analyzed simultaneously. 90% hidden faults were uncovered while 50% reduction in key operational faults was achieved.
Operators receive immediate notifications for critical faults and routine reports on rectification KPIs. Fault ticketing and tracking is inbuilt and provided by the advanced analytics platform.
Summary
Carnot Innovations has deployed their logic and machine learning based Fault Detection and Diagnostic (FDD) engine to identify operational, maintenance, design and energy saving opportunities at over 10 major Grade A commercial buildings in Hong Kong.
When
January 2020 - Ongoing
Make your Building Smarter
Together we can apply a data driven approach to building management with advanced machine learning fault detection and AI controls.
