System and method for detecting abnormality of air conditioning system

Abstract

A learning device trains an inference model to be a trained inference model using training data, the inference model being a model that infers a normal value of a specific parameter of an air conditioning system from operation data of the air conditioning system acquired in an operation period for which the air conditioning system is normal, of continuous first to N-th operation periods (N is the natural number), the training data including the operation data of the air conditioning system. An inference device, using the inference model, infers the normal value from operation data of the air conditioning system acquired in a (N+1)-th operation period. A determination device determines whether the air conditioning system in the (N+1)-th operation period is abnormal, based on comparison between the normal value and the specific parameter in the (N+1)-th operation period.

1 . A system for detecting an abnormality of an air conditioning system, the system comprising:
 a learning device configured to train an inference model to be a trained inference model using training data, the inference model being a model that infers a normal value of a specific parameter of the air conditioning system from operation data of the air conditioning system acquired in an operation period for which the air conditioning system is normal, of continuous first to N-th operation periods (N is the natural number), the training data including the operation data of the air conditioning system;   an inference device configured to, using the inference model, infer the normal value from operation data of the air conditioning system acquired in a (N+1)-th operation period; and   a determination device configured to determine whether the air conditioning system in the (N+1)-th operation period is abnormal, based on comparison between the normal value and the specific parameter in the (N+1)-th operation period, wherein   the learning device is configured to update the inference model using training data including operation data of the air conditioning system acquired in an operation period for which the air conditioning system is normal, of the continuous first to (N+1)-th operation periods,   the inference device is configured to, using the inference model, infer the normal value from operation data of the air conditioning system acquired in a (N+2)-th operation period, and   the determination device is configured to determine whether the air conditioning system in the (N+2)-th operation period is abnormal, based on comparison between the normal value and the specific parameter in the (N+2)-th operation period.   
     
     
         2 . The system according to  claim 1 , wherein
 the training data includes a simulation operation dataset acquired from a simulation result of the air conditioning system,   the learning device is configured to train the inference model to be the trained inference model using the simulation operation dataset,   the inference device is configured to infer the normal value from operation data of the air conditioning system acquired in a first operation period, and   the determination device is configured to determine whether the air conditioning system in the first operation period is abnormal, based on comparison between the normal value and the specific parameter in the first operation period.   
     
     
         3 . The system according to  claim 2 , wherein
 the learning device is configured to decrease a portion of the simulation operation dataset included in the training data, as normal operation data included in the training data increases.   
     
     
         4 . The system according to  claim 1 , wherein
 the air conditioning system includes an outdoor unit and at least one indoor unit,   the outdoor unit includes a compressor and a first heat exchanger,   each of the at least one indoor unit includes an expansion valve and a second heat exchanger,   refrigerant circulates in order of the compressor, the first heat exchanger, the expansion valve, and the second heat exchanger, or circulates in order of the compressor, the second heat exchanger, the expansion valve, and the first heat exchanger, and   the specific parameter includes at least one of an index value relating to heat transfer performance of the first heat exchanger and a flow rate coefficient of the expansion valve.   
     
     
         5 . A method for detecting an abnormality of an air conditioning system, the method comprising:
 building a trained inference model using training data, the inference model being a model that infers a normal value of a specific parameter of the air conditioning system from operation data of the air conditioning system acquired in an operation period for which the air conditioning system is normal, of continuous first to N-th operation periods (N is the natural number), the training data including the operation data;   using the inference model, inferring the normal value from operation data of the air conditioning system acquired in a (N+1)-th operation period;   determining whether the air conditioning system in the (N+1)-th operation period is abnormal, based on comparison between the normal value and the specific parameter in the (N+1)-th operation period;   updating the inference model using training data including operation data of the air conditioning system acquired in an operation period for which the air conditioning system is normal, of the continuous first to (N+1)-th operation periods;   using the inference model, inferring the normal value from operation data of the air conditioning system acquired in a (N+2)-th operation period; and   determining whether the air conditioning system in the (N+2)-th operation period is abnormal, based on comparison between the normal value and the specific parameter in the (N+2)-th operation period.