| Summary: | Modern buildings usually have an air-tight envelope. Therefore mechanical ventilation is very often necessary. A crucial part of the system is the filter, which allows creating an atmosphere that is free of dust, aerosols, and pollen. As organic material accumulates on the filter surface, the risk of micro-organism growth rises. This may yield health issues especially for the occupants of buildings.
The method was implemented in both a test rig and the HVAC system supplying different laboratories with fresh air in order to aggregate data for different abnormal and normal operation conditions. Subsequent considerations focuses on the test-rig measurements. The machine learning algorithm was trained successfully to detect anomalies of the filter behavior.
Finally, the change intervals of the filter may be adapted to the real degree of pollution without the requirement for visual observation in order to provide best air conditions. This algorithm is part of a general strategy for machine-learning processes for HVAC systems.
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