The Software firm CGnal, based in Milan, Italy, recently analyzed a year’s worth of data from the heating and ventilation units in an Italian hospitalm using input from sensors that are now commonly built into heating, ventilation and air conditioning units. The team had records such as temperature, humidity and electricity use, relating to appliances in operating theatres and first aid rooms as well as corridors, New Scientist reports.
Researchers trained a machine learning algorithm on data from the first half of 2015, looking for differences in the readings of similar appliances. They then tested it on data from the second half of the year and it predicted 76 out of 124 real faults, including 41 out of 44 where an appliance’s temperature rose above tolerable levels, with a false positive rate of 5 per cent.
“We started with the hospital because the heating, ventilation and air conditioning system is critical,” says Carlo Annis of building management firm eFM, which worked with CGnal on the experiment. These predictive algorithms could help fix faults before facilities crash – avoiding unnecessary work at the same time.
“It’s a nice technique that can be applied to existing telemetry [remote sensing] datasets,” says David Shipworth of University College London. But the differences in function may become less distinct as the units age, he says. “The barrier between normal and faulty operation will become more and more blurred.”