Anomaly detection for refrigeration systems

Abstract

Methods and systems are described for anomaly detection in refrigeration systems. A process for providing anomaly detection for refrigeration systems includes receiving telemetry data of one or more refrigeration systems, including measured temperature values and setpoint temperature values; processing the telemetry data to determine machine learning input data based at least in part on at least a portion of the measured temperature values and at least a portion of the setpoint temperature values; and using one or more hardware processors to apply the machine learning input data to a trained anomaly detection machine learning model to determine periodic anomaly metrics. The process provides an automatically determined indication based at least in part on at least a portion of the periodic anomaly metrics.

1 . A method, comprising:
 receiving telemetry data associated with one or more refrigeration systems over a first time interval, wherein the telemetry data includes a set of temperature measurements;   executing an anomaly detection machine-learning model using the telemetry data, wherein the anomaly detection machine-learning model, predicts an occurrence of an anomaly event in a refrigeration system occurring over a future time interval;   generating a work order for the refrigeration system configured to prevent the occurrence of the anomaly event;   validating the anomaly detection machine-learning model by correlating the work order with the anomaly event; and   retraining the anomaly detection machine-learning model.   
     
     
         2 . The method of  claim 1 , wherein the telemetry data is collected by one or more sensors associated with the one or more refrigeration systems. 
     
     
         3 . The method of  claim 2 , wherein at least one of the one or more sensors is configured to measure an ambient condition external to the one or more refrigeration systems. 
     
     
         4 . The method of  claim 1 , wherein generating the telemetry data is modified using one or more of forward filling, normalizing values, or linear interpolation. 
     
     
         5 . The method of  claim 1 , further comprising:
 modifying the telemetry data by subtracting temperature values by a setpoint temperature value, wherein the anomaly detection machine-learning model executes using the modified telemetry data.   
     
     
         6 . The method of  claim 1 , wherein validating the anomaly detection machine-learning model determines whether the anomaly event is a false positive. 
     
     
         7 . The method of  claim 1 , wherein retraining the anomaly detection machine-learning model is based on validating the anomaly detection machine-learning model. 
     
     
         8 . A system, comprising:
 one or more processors; and   a non-transitory computer-readable medium storing instructions that when executed by the one or more processors, cause the one or more processors to perform operations including:
 receiving telemetry data associated with one or more refrigeration systems over a first time interval, wherein the telemetry data includes a set of temperature measurements; 
 executing an anomaly detection machine-learning model using the telemetry data, wherein the anomaly detection machine-learning model, predicts an occurrence of an anomaly event in a refrigeration system occurring over a future time interval; 
 generating a work order for the refrigeration system configured to prevent the occurrence of the anomaly event; 
 validating the anomaly detection machine-learning model by correlating the work order with the anomaly event; and 
 retraining the anomaly detection machine-learning model. 
   
     
     
         9 . The system of  claim 8 , wherein the telemetry data is collected by one or more sensors associated with the one or more refrigeration systems. 
     
     
         10 . The system of  claim 9 , wherein at least one of the one or more sensors is configured to measure an ambient condition external to the one or more refrigeration systems. 
     
     
         11 . The system of  claim 8 , wherein generating the telemetry data is modified using one or more of forward filling, normalizing values, or linear interpolation. 
     
     
         12 . The method of  claim 1 , further comprising:
 modifying the telemetry data by subtracting temperature values by a setpoint temperature value, wherein the anomaly detection machine-learning model executes using the modified telemetry data.   
     
     
         13 . The system of  claim 8 , wherein validating the anomaly detection machine-learning model determines whether the anomaly event is a false positive. 
     
     
         14 . The system of  claim 8 , wherein retraining the anomaly detection machine-learning model is based on validating the anomaly detection machine-learning model. 
     
     
         15 . A non-transitory computer-readable medium storing instructions that when executed by one or more processors, cause the one or more processors to perform operations including:
 receiving telemetry data associated with one or more refrigeration systems over a first time interval, wherein the telemetry data includes a set of temperature measurements;   executing an anomaly detection machine-learning model using the telemetry data, wherein the anomaly detection machine-learning model, predicts an occurrence of an anomaly event in a refrigeration system occurring over a future time interval;   generating a work order for the refrigeration system configured to prevent the occurrence of the anomaly event;   validating the anomaly detection machine-learning model by correlating the work order with the anomaly event; and   retraining the anomaly detection machine-learning model.   
     
     
         16 . The non-transitory computer-readable medium of  claim 15 , wherein the telemetry data is collected by one or more sensors associated with the one or more refrigeration systems. 
     
     
         17 . The non-transitory computer-readable medium of  claim 16 , wherein at least one of the one or more sensors is configured to measure an ambient condition external to the one or more refrigeration systems. 
     
     
         18 . The non-transitory computer-readable medium of  claim 15 , wherein generating the telemetry data is modified using one or more of forward filling, normalizing values, or linear interpolation. 
     
     
         19 . The non-transitory computer-readable medium of  claim 15 , further comprising:
 modifying the telemetry data by subtracting temperature values by a setpoint temperature value, wherein the anomaly detection machine-learning model executes using the modified telemetry data.   
     
     
         20 . The non-transitory computer-readable medium of  claim 15 , wherein validating the anomaly detection machine-learning model determines whether the anomaly event is a false positive.