Abstract: According to one embodiment, an anomaly sign detection system comprising one or more computers configured to: calculate a correction value for correcting at least one actual process value from the at least one actual process value and at least one reference process value; determine whether each of plurality of actual process values is correlated with the at least one reference process value or not, based on correction-necessity coefficient of determination; use the correction value for correcting at least one actual process value determined to be correlated with the at least one reference process value among the plurality of actual process values; generate learning input data including at least one corrected process value as the at least one actual process value corrected by the correction value; and perform machine learning by inputting the learning input data to anomaly sign detection model.

Abstract: According to one embodiment, an anomaly sign detection system comprising one or more computers configured to: calculate a correction value for correcting at least one actual process value from the at least one actual process value and at least one reference process value; determine whether each of plurality of actual process values is correlated with the at least one reference process value or not, based on correction-necessity coefficient of determination; use the correction value for correcting at least one actual process value determined to be correlated with the at least one reference process value among the plurality of actual process values; generate learning input data including at least one corrected process value as the at least one actual process value corrected by the correction value; and perform machine learning by inputting the learning input data to anomaly sign detection model.

Abstract: According to one embodiment, a monitoring apparatus includes a processing circuit. The processing circuit is configured to generate second data including a prediction value of a second sensor correlated with a first sensor from first data including a measurement value of the first sensor of which a measurement value changes suddenly in a case where the control signal changes, detect an anomaly of the system or an anomaly of at least one sensor, and make it difficult to detect the anomaly in a case where the determination signal indicates that there is a change in the control signal.

Abstract: According to one embodiment, an abnormality sign detection system comprising one or more computers configured to perform machine learning of an abnormality-sign detection-model that detects at least one of an abnormality in a target facility to be monitored and a sign of the abnormality, wherein the one or more computers are configured to: acquire a plurality of process amounts generated at the target facility; classify each of the plurality of process amounts into either correlation data for which correlation between the plurality of process amounts is learned or decorrelation data for which correlation between the plurality of process amounts is not learned; generate learning input data depending on classification, the learning input data being data in which each of the plurality of process amounts is associated with the correlation data or the decorrelation data; and perform the machine learning by inputting the learning input data to the abnormality-sign detection-model.

Abstract: According to an embodiment, a monitoring apparatus configured to generate time-series predicted data based on time-series measured data and a prediction model that generates predicted data including one or more predicted values predicted to be output from one or more sensors; and generate, for a first sensor among the one or more sensors, a displayed image including a measured value graph representing a temporal change in a measured value included in the time-series measured data in a second period after a first period, a predicted value graph representing a temporal change in a predicted value included in time-series predicted data in the second period, past distribution information representing a distribution of a measured value in the first period, and measurement distribution information representing a distribution of the measured value included in the time-series measured data in the second period.

Abstract: According to one embodiment, a monitoring apparatus includes a processing circuit. The processing circuit is configured to generate second data including a prediction value of a second sensor correlated with a first sensor from first data including a measurement value of the first sensor of which a measurement value changes suddenly in a case where the control signal changes, detect an anomaly of the system or an anomaly of at least one sensor, and make it difficult to detect the anomaly in a case where the determination signal indicates that there is a change in the control signal.

Abstract: According to one embodiment, a processing circuit classifies a time-series data corresponding to process amounts generated in a target facility into groups. For each of groups, the processing circuit applies time-series data included in the group to a first auto-encoder, which differs depending upon each group, and outputs time-series data. The processing circuit applies input difference data, which are based on output time-series data on the process amounts and the input time-series data, to a single second auto-encoder, and outputs difference data. The processing circuit determines an abnormality of the target facility, based on the comparison between addition data which are based on the output difference data and the output time-series data, and the input time-series data.

Abstract: According to an embodiment, a monitoring apparatus configured to generate time-series predicted data based on time-series measured data and a prediction model that generates predicted data including one or more predicted values predicted to be output from one or more sensors; and generate, for a first sensor among the one or more sensors, a displayed image including a measured value graph representing a temporal change in a measured value included in the time-series measured data in a second period after a first period, a predicted value graph representing a temporal change in a predicted value included in time-series predicted data in the second period, past distribution information representing a distribution of a measured value in the first period, and measurement distribution information representing a distribution of the measured value included in the time-series measured data in the second period.