Systems and methods for controlling variable refrigerant flow systems and equipment using artificial intelligence models

Abstract

An oil management controller for heating, ventilation, or air conditioning (HVAC) equipment. The controller includes a processing circuit. The processing circuit is configured to analyze operating data for the HVAC equipment using a machine learning model to predict a variable state or condition of oil used by the HVAC equipment. The processing circuit is configured to identify an oil deficiency based on the variable state or condition of the oil. The processing circuit is configured to automatically initiate a corrective action responsive to identifying the oil deficiency.

1 - 40 . (canceled) 
     
     
         41 . A controller for predicting faults in a heating, ventilation, or air conditioning (HVAC) system, the controller comprising a processing circuit configured to:
 analyze operating data for the HVAC system using a machine learning model to predict a fault classification for the HVAC system, the fault classification identifying a fault condition affecting the HVAC system;   identify a HVAC device of the HVAC system associated with the fault condition; and   automatically initiate a corrective action to address the fault condition responsive to identifying the HVAC device and the fault condition.   
     
     
         42 . The controller of  claim 41 , wherein:
 the fault classification includes a severity metric associated with the fault condition, the severity metric indicating a degree of influence that the fault condition has on the HVAC system; and   the corrective action is determined based on both the fault condition and the severity metric associated with the fault condition.   
     
     
         43 . The controller of  claim 42 , wherein the processing circuit is configured to:
 automatically initiate a first corrective action in response to a value of the severity metric being below a severity threshold; and   automatically initiate a second corrective action in response to the value of the severity metric being above the severity threshold.   
     
     
         44 . The controller of  claim 43 , wherein:
 the first corrective action comprises providing a notification to a user device; and   the second corrective action comprises scheduling maintenance for the HVAC system or replacement of the HVAC device associated with the fault condition.   
     
     
         45 . The controller of  claim 42 , wherein the corrective action comprises taking no action in response to a value of the severity metric being below a severity threshold. 
     
     
         46 . The controller of  claim 41 , wherein
 the machine learning model is a recurrent neural network (RNN) model; and   analyzing the operating data comprises providing a time series of values of the operating data as an input to the RNN model and obtaining a prediction of the fault classification as an output of the RNN model.   
     
     
         47 . The controller of  claim 41 , the processing circuit further configured to generate the machine learning model using a set of simulated training data obtained from a simulation model of the HVAC system. 
     
     
         48 . The controller of  claim 41 , wherein the fault classification identifies a plurality of fault conditions affecting the HVAC system, the plurality of fault conditions associated with a plurality of HVAC devices of the HVAC system. 
     
     
         49 . The controller of  claim 41 , wherein the fault condition comprises at least one of:
 leakage of a refrigerant;   frosting of an outdoor unit;   clogging of an indoor fan;   clogging of an indoor filter;   clogging of a heat exchanger;   clogging of an outdoor fan;   demagnetization of a motor; or   leakage of oil from a compressor.   
     
     
         50 . The controller of  claim 41 , wherein the machine learning model is a first machine learning model and the processing circuit is configured to:
 use the first machine learning model to predict the fault classification for the HVAC system; and   use a second machine learning model to predict a severity of the fault condition identified by the fault classification.   
     
     
         51 . A method for predicting faults in a heating, ventilation, or air conditioning (HVAC) system, the method comprising:
 analyzing operating data for the HVAC system using a machine learning model to predict a fault classification for the HVAC system, the fault classification identifying a fault condition affecting the HVAC system;   identifying a HVAC device of the HVAC system associated with the fault condition; and   automatically initiating a corrective action to address the fault condition responsive to identifying the HVAC device and the fault condition.   
     
     
         52 . The method of  claim 51 , wherein:
 the fault classification includes a severity metric associated with the fault condition, the severity metric indicating a degree of influence that the fault condition has on the HVAC system; and   the corrective action is determined based on both the fault condition and the severity metric associated with the fault condition.   
     
     
         53 . The method of  claim 52 , comprising:
 automatically initiating a first corrective action in response to a value of the severity metric being below a severity threshold; and   automatically initiating a second corrective action in response to the value of the severity metric being above the severity threshold.   
     
     
         54 . The method of  claim 53 , wherein:
 the first corrective action comprises providing a notification to a user device; and   the second corrective action comprises scheduling maintenance for the HVAC system or replacement of the HVAC device associated with the fault condition.   
     
     
         55 . The method of  claim 52 , wherein the corrective action comprises taking no action in response to a value of the severity metric being below a severity threshold. 
     
     
         56 . The method of  claim 51 , wherein
 the machine learning model is a recurrent neural network (RNN) model; and   analyzing the operating data comprises providing a time series of values of the operating data as an input to the RNN model and obtaining a prediction of the fault classification as an output of the RNN model.   
     
     
         57 . The method of  claim 51 , comprising generating the machine learning model using a set of simulated training data obtained from a simulation model of the HVAC system. 
     
     
         58 . The method of  claim 51 , wherein the fault classification identifies a plurality of fault conditions affecting the HVAC system, the plurality of fault conditions associated with a plurality of HVAC devices of the HVAC system. 
     
     
         59 . The method of  claim 51 , wherein the fault condition comprises at least one of:
 leakage of a refrigerant;   frosting of an outdoor unit;   clogging of an indoor fan;   clogging of an indoor filter;   clogging of a heat exchanger;   clogging of an outdoor fan;   demagnetization of a motor; or   leakage of oil from a compressor.   
     
     
         60 . The method of  claim 51 , wherein the machine learning model is a first machine learning model and the method comprises:
 using the first machine learning model to predict the fault classification for the HVAC system; and   using a second machine learning model to predict a severity of the fault condition identified by the fault classification.