Abstract: A data prediction method and apparatus generate plant normal state prediction data based on measurement data of multiple tags and a plant prediction model, to enhance accuracy of anomaly/fault prediction by providing precise prediction data in the normal state even in a plant anomaly/fault condition. The method includes generating primary prediction data by performing primary prediction based on the measurement data and the plant prediction model; selecting an anomalous state tag among the multiple tags, the selected anomalous state tag determined as data of an anomalous state based on measurement data corresponding to the primary prediction data; updating the plant prediction model by using the measurement data of only normal state tags; and generating secondary prediction data by performing secondary prediction based on the measurement data of the normal state tags and the updated plant prediction model. Secondary prediction is performed only when an anomalous state tag is selected.

Abstract: Disclosed is a fault signal recovery system including a data processor configured to generate a signal subset U* by removing, from a signal set U for a plurality of tags, some tags including a fault signal, and a first learning signal subset X* by removing tags disposed at positions corresponding to the some tags from a learning signal set X containing only tags of normal signals, a modeling unit configured to generate feature information F extractable from the first learning signal subset X* and recovery information P on a plurality of recovery models usable for restoring the fault signal, and a recovery unit configured to estimate and recover normal signals for the some tags based on the signal subset U*, the first learning signal subset X*, the feature information F, the recovery information P on the plurality of recovery models, and similarity Z.

Abstract: A method of predicting plant data in a system generates prediction data based on a plant prediction model and for detecting anomalies of the plant by comparing the prediction data with measurement data. The method can provide precise prediction data in a normal state even though the measurement data contains data in an anomalous state. Anomaly/fault prediction accuracy is enhanced by providing precise prediction data in the normal state. An apparatus using the method includes a plant modeling unit for generating a plant prediction model based on k-nearest neighbors (k-NN) by modeling a plant based on measurement data of multiple tags; and a prediction data generating unit for measuring similarity between the plant prediction model and the measurement data, determining a parameter k value based on the similarity, and generating plant normal state prediction data based on the determined parameter k value and the measured similarity.

Abstract: A system and method for configuring a boiler combustion model are provided. The system for configuring the boiler combustion model may include a model generator configured to generate the boiler combustion model using, as input/output data, data obtained based on measured data, analysis data, and controller information, a model simulator configured to simulate the generated boiler combustion model and output simulated results, and a model modifier configured to evaluate the boiler combustion model based on the simulated results and generate modification information for modifying the boiler combustion model based on the generated boiler combustion model and corresponding evaluated results.

Abstract: A system and method for predicting an analytical abnormality are provided. The method for predicting an analytical abnormality may include generating a signal generation model and an analysis model for a design object based on first analysis data, applying a signal generated by the signal generation model to the analysis model based on second analysis data to calculate one or more estimated values, comparing the estimated values and the second analysis data to generate a plurality of early warning information, and determining whether to output an early warning based on whether the plurality of early warning information satisfy a preset condition.

Abstract: A system for controlling a boiler apparatus in a power plant to combust under optimized conditions, and a method for optimizing combustion of the boiler apparatus using the same are provided.

Abstract: An apparatus for managing combustion optimization is provided. The apparatus for managing combustion optimization includes a data collector configured to collect real-time data that is measured in real time from a boiler system including a boiler and a combustion controller configured to control combustion of the boiler, a management configured to determine whether to perform combustion optimization of the boiler based on the real-time data, and an executor configured to generate a control command and transmit the control command to the combustion controller to perform the combustion optimization of the boiler in response to determining that the combustion optimization of the boiler is possible.

Abstract: An apparatus and method for generating learning data for combustion optimization is provided. The apparatus includes a data pre-processor to collect raw data including currently measured real-time data for boiler combustion and previously measured past data for the boiler combustion, and to perform pre-processing for the collected raw data, and a data analyzer to derive learning data from the raw data by analyzing the raw data. An apparatus for combustion optimization includes a management layer to collect currently measured real-time data for boiler combustion, to determine whether to perform combustion optimization, and to determine whether to tune a combustion model and a combustion controller; a data layer to derive learning data from raw data; a model layer to generate the combustion model/controller through the learning data; and an optimal layer to calculate a target value for combustion optimization and to output a control signal according to the calculated target value.

Abstract: An apparatus for combustion optimization is provided. The apparatus for combustion optimization includes a management layer configured to collect currently measured real-time data for boiler combustion, and to determine whether to perform combustion optimization and whether to tune a combustion model and a combustion controller by analyzing the collected real-time data, a data layer configured to derive learning data necessary for designing the combustion model and the combustion controller from the real-time data and previously measured past data for the boiler combustion, a model layer configured to generate the combustion model and the combustion controller through the learning data, and an optimal layer configured to calculate a target value for the combustion optimization by using the combustion model and the combustion controller, and to output a control signal according to the calculated target value.

Abstract: An apparatus and method for diagnosing analysis is provided. The apparatus includes an analytic layer to divide a peripheral space of a target component into a plurality of cells and to derive analytic data by performing a numerical analysis iteration according to computational fluid dynamics for the plurality of cells; a model layer to derive an analytic model that simulates the numerical analysis iteration; a predictive layer to derive predictive data by predicting a result of the numerical analysis iteration by using the analytic model; and a diagnostic layer to diagnose an abnormality condition of numerical analysis by comparing the analytic data and predictive data during the numerical analysis iteration performed by the analytic layer. The diagnostic layer includes an early alarm to generate early alarm information by sorting a cell satisfying an early alarm condition; and an abnormality diagnostic device to determine whether the numerical analysis iteration is abnormal.

Abstract: A system for generating learning data is provided. The system for generating the learning data includes a data incorporating part configured to generate new data by filtering plant data based on a warning condition to incorporate the plant data for one configuration of a plant into existing learning data and a learning data generating part configured to differentiate a weight applied to the new data and the existing learning data, respectively, by comparing the number of the new data and the number of the existing learning data, and generate new learning data by combining the new data with the existing learning data to which the weight is applied.

Abstract: A system and method predict whether a plant is abnormal by modeling a relationship equation between tags based on a correlation between the tags, applicable even if modeling is executed without understanding a target to abnormality determination, and implements internal early alarm logic based on a difference between measured data and predicted data over time. The plant abnormality prediction system includes a modeling information output unit including a pre-processing part for pre-processing past data received for a plurality of tags, a correlation analysis part for receiving the pre-processed data for each tag to determine an independent tag among the plurality of tags based on correlation coefficients for any two tags, and a modeling part for generating a relationship equation between the tags by using outputs of the pre-processing part and the correlation analysis part; and a prediction unit for calculating estimated data for the tag based on the relationship equation.

Abstract: A system and method predict whether or not a plant is abnormal and perform an accurate prediction even if a modeling is executed in a state where the understanding for a target to abnormality determination is low, or when a person unfamiliar with system designs a prediction model. The system includes a correlation coefficient calculation unit for calculating a correlation coefficient for each of two tags among a plurality of tags; a relevant tag determination unit for determining a relevant tag for each tag of the plurality of tags by comparing the correlation coefficient with a reference value; and an independent tag determination unit for determining one or more among the plurality of tags as an independent tag based on the relevant tag. The relevant tag determination unit includes primary and second tag extraction sections for extracting primary and second tags for each tag of the plurality of tags.

Abstract: The present disclosure provides a plant abnormality detection system and method, which can learn the plant data collected in real time through a plurality of prediction models having different characteristics to generate a prediction value having the highest accuracy to diagnose the abnormality thereof, thus detecting accurately the abnormality of the plant to early provide alarm.

Abstract: A method of predicting plant data in a system generates prediction data based on a plant prediction model and for detecting anomalies of the plant by comparing the prediction data with measurement data. The method can provide precise prediction data in a normal state even though the measurement data contains data in an anomalous state. Anomaly/fault prediction accuracy is enhanced by providing precise prediction data in the normal state. An apparatus using the method includes a plant modeling unit for generating a plant prediction model based on k-nearest neighbors (k-NN) by modeling a plant based on measurement data of multiple tags; and a prediction data generating unit for measuring similarity between the plant prediction model and the measurement data, determining a parameter k value based on the similarity, and generating plant normal state prediction data based on the determined parameter k value and the measured similarity.

Abstract: A data prediction method and apparatus generate plant normal state prediction data based on measurement data of multiple tags and a plant prediction model, to enhance accuracy of anomaly/fault prediction by providing precise prediction data in the normal state even in a plant anomaly/fault condition. The method includes generating primary prediction data by performing primary prediction based on the measurement data and the plant prediction model; selecting an anomalous state tag among the multiple tags, the selected anomalous state tag determined as data of an anomalous state based on measurement data corresponding to the primary prediction data; updating the plant prediction model by using the measurement data of only normal state tags; and generating secondary prediction data by performing secondary prediction based on the measurement data of the normal state tags and the updated plant prediction model. Secondary prediction is performed only when an anomalous state tag is selected.

Abstract: Disclosed is a fault signal recovery system including a data processor configured to generate a signal subset U* by removing, from a signal set U for a plurality of tags, some tags including a fault signal, and a first learning signal subset X* by removing tags disposed at positions corresponding to the some tags from a learning signal set X containing only tags of normal signals, a modeling unit configured to generate feature information F extractable from the first learning signal subset X* and recovery information P on a plurality of recovery models usable for restoring the fault signal, and a recovery unit configured to select an optimum recovery model by matching the feature of the learning signal set X with the recovery models generated through the recovery in formation P to estimate and recover normal signals for the some tags.

Abstract: Disclosed is a fault signal recovery system including a data processor configured to generate a signal subset U* by removing, from a signal set U for a plurality of tags, some tags including a fault signal, and a first learning signal subset X* by removing tags disposed at positions corresponding to the some tags from a learning signal set X containing only tags of normal signals, a modeling unit configured to generate feature information F extractable from the first learning signal subset X* and recovery information P on a plurality of recovery models usable for restoring the fault signal, and a recovery unit configured to estimate and recover normal signals for the some tags based on the signal subset U*, the first learning signal subset X*, the feature information F, the recovery information P on the plurality of recovery models, and similarity Z.

Abstract: A fault signal recovery apparatus and method for collecting signals obtained in a plant and recovering normal signals from fault signals contained in the measured signals through a machine learning method includes receiving an input X including only normal signals for a plurality of tags, an input U including fault signals for a first group of tags among the plurality of tags and normal signals for a second group of tags, and an input S having information on the first group of tags including fault signals, and estimating and recovering normal signals for the first group of tags including fault signals based on feature information F, recovery model information P, and ensemble learning.

Abstract: The present invention relates to a pharmaceutical composition for treating and preventing inflammatory diseases comprising an ethyl acetate fraction of dried extract of Trachelospermi caulis as an active ingredient and a method for producing the fraction. More particularly, the present invention relates to a composition for preventing and treating inflammatory diseases comprising an ethyl acetate fraction of dried extract of Trachelospermi caulis as an active ingredient, in which the extract of Trachelospermi caulis is refined and concentrated to contain 0.05˜12% by weight of arctigenin as an index material, and a method for producing the fraction.