AC anomaly detection cools off a hot issue
The project started with gaining a better understanding of the factors that could inhibit air conditioner performance. This research phase included identifying what conditions could impair performance of a unit. Things like fan motor issues, frozen evaporator coils, blocked air filters or vents, dirty condensers, and compressor cycle issues. With these understood as the most common problems with AC function, the team turned to collecting appropriate data that could lead to fault detection and identification (see figure below).
Our data scientists used Eaton Smart Breakers data to first establish performance profile for an AC unit. These included:
Any combination of these issues could then be mapped against the known possible faults established in our initial research. This summary could then be sent to the consumer or manufacturer to address before the anomaly lead to a critical failure.
Implementation of this data-driven solution has benefits for consumers and manufacturers including energy conservation, increased safety, improved reliability, and increased comfort for consumers.