Method For Updating Model Of Facility Monitoring System

1 . A method of updating a model of a facility monitoring system, the method comprising:

checking occurrence of an event;

when the event occurs, retraining a neural network model that performs a failure diagnosis;

extracting a previous threshold value of the neural network model;

calculating a new threshold value based on the extracted previous threshold value and a median value of state variables calculated through the retraining of the neural network model; and

updating the threshold value of the neural network model based on the new threshold value.

2 . The method of claim 1 , wherein the checking of the occurrence of the event includes checking the occurrence of the event including at least one of facility failure, facility repair, facility replacement, facility addition, facility removal, and user request.

3 . The method of claim 1 , wherein the checking of the occurrence of the event includes checking the occurrence of the event in which a current failure class is the same as a past failure class.

4 . The method of claim 3 , wherein the checking of the occurrence of the event includes diagnosing a current failure based on upper parameters of which the state variables are larger than a reference value, and confirming that the event occurs when it is determined that the current failure class is the same as the past failure class.

5 . The method of claim 1 , wherein the retraining of the neural network model includes retraining the neural network model based on an autoencoder.

6 . The method of claim 1 , wherein the retraining of the neural network model includes acquiring sensor data output from each sensor and inputting input data including the sensor data of each sensor to the neural network model to retrain the neural network model.

7 . The method of claim 1 , wherein the retraining of the neural network model includes acquiring sensor data output from each sensor, acquiring output data by inputting input data including the sensor data of each sensor to the neural network model, and calculating state variables including a Health Index (HI) by comparing the input data and the output data.

8 . The method of claim 1 , wherein the extracting of the previous threshold value of the neural network model includes extracting only the previous threshold value of the neural network model when there is no threshold value update history of the neural network model.

9 . The method of claim 1 , wherein the extracting of the previous threshold value of the neural network model includes extracting an initial threshold value and all of updated threshold values of the neural network model when there is a threshold value update history of the neural network model.

10 . The method of claim 1 , wherein the extracting of the previous threshold value of the neural network model includes extracting a past threshold value of the neural network model corresponding to the past failure class when the current failure class is the same as the past failure class as a result of the failure diagnosis.

11 . The method of claim 1 , wherein the calculating of the new threshold value includes, when there is no threshold value update history of the neural network model, calculating the new threshold value based on the previous threshold value of the neural network model, a first median value of state variables corresponding to the first training section in the retraining of the neural network model, a second median value of state variables corresponding to the second training section, and a correction value for failure cost.

12 . The method of claim 1 , wherein the calculating of the new threshold value includes, when there is a threshold value update history of the neural network model, calculating the new threshold value based on an initial threshold value and all updated threshold values of the neural network model, a first median value of state variables corresponding to the first training section in the retraining of the neural network model, a second median value of state variables corresponding to the second training section, and a correction value for failure cost.

13 . The method of claim 1 , wherein the calculating of the new threshold value includes, when a current failure class is the same as a past failure class, calculating the new threshold value based on a past threshold value of the neural network model corresponding to the past failure class and a past median value of state variables corresponding to a normal section, a current threshold value of the neural network model corresponding to the current failure class and a current median value of state variables corresponding to the normal section, and a correction value for failure cost.

14 . A computer program stored in a computer readable storage medium, wherein when the computer program is executed by one or more processors, the computer program performs following operations for updating a model, the operations comprising:

checking occurrence of an event;

when the event occurs, retraining a neural network model that performs a failure diagnosis;

extracting a previous threshold value of the neural network model;

calculating a new threshold value based on the extracted previous threshold value and a median value of state variables calculated through the retraining of the neural network model; and

updating the threshold value of the neural network model based on the new threshold value.

15 . A computing device for providing a method of updating a model, the computing device comprising:

a processor including one or more cores; and

a memory,

wherein the processor checks occurrence of an event,

retrains a neural network model that performs a failure diagnosis when the event occurs,

extracts a previous threshold value of the neural network model,

calculates a new threshold value based on the extracted previous threshold value and a median value of state variables calculated through the retraining of the neural network model, and

updates the threshold value of the neural network model based on the new threshold value.