Abstract: An exemplary embodiment of the present disclosure discloses a method of setting a model threshold value for detecting an anomaly of a facility monitoring system, the method including: acquiring sensor data output from each sensor; extracting a feature value for the sensor data of each sensor; acquiring output data by inputting input data including the extracted feature value to a trained neural network model; and comparing the input data and the output data and setting a threshold value for detecting an anomaly based on a calculated comparison result value.

Abstract: Disclosed is a method of updating a model of a facility monitoring system, the method including: 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.

Abstract: An exemplary embodiment of the present disclosure discloses a method of setting a model threshold value for detecting an anomaly of a facility monitoring system, the method including: acquiring sensor data output from each sensor; extracting a feature value for the sensor data of each sensor; acquiring output data by inputting input data including the extracted feature value to a trained neural network model; and comparing the input data and the output data and setting a threshold value for detecting an anomaly based on a calculated comparison result value.

Abstract: An exemplary embodiment of the present disclosure discloses a method of classifying facility failure of a facility monitoring system, the method including: acquiring sensor data output from each sensor; acquiring output data by inputting input data including the sensor data of each sensor to a trained neural network model; calculating a comparison result value by comparing the input data and the output data; diagnosing failure based on specific parameters included in the comparison result value; and classifying failure corresponding to the input data based on a combination of the specific parameters.