Learning device, monitoring device, and air conditioning system

Abstract

Operation data of an air conditioning apparatus includes a first data group and a second data group that is not the same as the first data group. A learning device includes: a first calculation unit configured to calculate a first feature amount from the first data group of the air conditioning apparatus during a learning period; and a learning unit configured to generate a first inference model that infers a first normal range of the first feature amount from a second data group by performing supervised learning using the second data group with the first feature amount being set as truth data, the first feature amount being obtained by calculation by the first calculation unit.

1 . A learning device configured to learn a condition of an air conditioning apparatus in which refrigerant circulates, wherein operation data of the air conditioning apparatus includes a first data group and a second data group that does not contain a same data element as a data element of the first data group,
 the learning device comprising:   a first calculation unit configured to calculate a first feature amount from the first data group of the air conditioning apparatus during a learning period; and   a learning unit configured to generate a first inference model that infers a first normal range of the first feature amount from the second data group by performing supervised learning using the second data group with the first feature amount being set as truth data.   
     
     
         2 . The learning device according to  claim 1 , wherein
 the operation data includes a third data group different from the first data group and a fourth data group that does not contain a same data element as a data element of the third data group,   the first calculation unit is configured to calculate a second feature amount from the third data group during the learning period, and   the learning unit is configured to generate a second inference model that infers a second normal range of the second feature amount from the fourth data group by performing supervised learning using the fourth data group with the second feature amount being set as truth data, the second feature amount being obtained by the calculation by the first calculation unit.   
     
     
         3 . A monitoring device for an air conditioning apparatus, the monitoring device using the first inference model and the second inference model each generated by the learning device according to  claim 2 , each of the first inference model and the second inference model being trained,
 the monitoring device comprising:   a second calculation unit configured to calculate the first feature amount from the first data group and the second feature amount from the third data group among the operation data of the air conditioning apparatus during a monitoring period;   an inference unit configured to output, from the second data group among the operation data of the air conditioning apparatus during the monitoring period, the first normal range using the first inference model trained, and to output the second normal range from the fourth data group among the operation data of the air conditioning apparatus during the monitoring period; and   a determination unit configured to determine whether or not the condition of the air conditioning apparatus is normal based on a frequency at which a first poor condition and a second poor condition are simultaneously detected, the first poor condition being a condition in which the first feature amount calculated by the second calculation unit falls out of the first normal range inferred by the inference unit, the second poor condition being a condition in which the second feature amount calculated by the second calculation unit falls out of the second normal range inferred by the inference unit.   
     
     
         4 . The monitoring device according to  claim 3 , wherein
 the air conditioning apparatus includes a refrigerant circuit in which the refrigerant circulates,   the refrigerant circuit includes a compressor, a condenser, an expansion valve, and an evaporator,   the first feature amount is a degree of supercooling of the refrigerant at an outlet of the condenser, and   the second feature amount is a heat exchange performance of the condenser.   
     
     
         5 . The monitoring device according to  claim 4 , wherein
 the first data group at least includes a pressure of the refrigerant discharged from the compressor and a temperature of the refrigerant at the outlet of the condenser,   the second data group at least includes a temperature of an air conditioning target space, an operation frequency of the compressor, and a degree of opening of the expansion valve,   the third data group at least includes the pressure of the refrigerant discharged from the compressor, the temperature of the refrigerant at the outlet of the condenser, and the operation frequency of the compressor, and   the fourth data group at least includes the temperature of the air conditioning target space and the degree of opening of the expansion valve.   
     
     
         6 . The monitoring device according to  claim 3 , further comprising a display unit configured to display a change in the frequency per certain period elapsed and configured to display that maintenance is necessary when the frequency exceeds a determination threshold value. 
     
     
         7 . A monitoring device for an air conditioning apparatus, the monitoring device using the first inference model generated by the learning device according to  claim 1 , the first inference model being trained,
 the monitoring device comprising:   a second calculation unit configured to calculate the first feature amount from the first data group among the operation data of the air conditioning apparatus during a monitoring period;   an inference unit configured to output, using the first inference model trained, the first normal range from the second data group among the operating data of the air conditioning apparatus during the monitoring period; and   a determination unit configured to determine whether or not the condition of the air conditioning apparatus is normal by comparing the first feature amount calculated by the second calculation unit with the first normal range inferred by the inference unit.   
     
     
         8 . The monitoring device according to  claim 7 , wherein
 the air conditioning apparatus includes a refrigerant circuit in which the refrigerant circulates,   the refrigerant circuit includes a compressor, a condenser, an expansion valve, and an evaporator, and   the first feature amount is a degree of supercooling of the refrigerant at an outlet of the condenser.   
     
     
         9 . The monitoring device according to  claim 8 , wherein
 the first data group includes a pressure of the refrigerant discharged from the compressor and a temperature of the refrigerant at the outlet of the condenser, and   the second data group at least includes a temperature of an air conditioning target space and an operation frequency of the compressor.   
     
     
         10 . An air conditioning system comprising:
 the monitoring device according to  claim 3 ; and   the air conditioning apparatus.   
     
     
         11 . An air conditioning system comprising:
 the monitoring device according to  claim 7 ; and   the air conditioning apparatus.