Abstract: An information processing device includes a relationship identifier that identifies a first causal relationship based on data of a connection state and an order relationship of machineries that constitute a manufacturing line, the first causal relationship being between the machineries in a process performed on the manufacturing line, a relationship acquirer that acquires a second causal relationship between events related to the machineries in a process performed on the manufacturing line according to user setting, and a relationship synthesizer that generates a third causal relationship obtained by integrating the first causal relationship and the second causal relationship.

Abstract: A control system includes: a generation unit that generates a dataset for each unit section; a feature extraction unit that generates feature quantity data on the basis of the dataset; and a score calculation unit that calculates a score indicating a degree of deviation of the feature quantity data from learning data, by referring to the learning data. The feature quantity data and the score are output as compression results of the dataset. The control system includes a restoration unit that selects pattern data corresponding to a class determined according to the score contained in the compression result, and after adjusting the pattern data using the feature quantity data contained in the compression results, restores the pattern data as a temporal change in the dataset corresponding to the compression results.

Abstract: An information processing device includes a relationship identifier that identifies a first causal relationship based on data of a connection state and an order relationship of machineries that constitute a manufacturing line, the first causal relationship being between the machineries in a process performed on the manufacturing line, a relationship acquirer that acquires a second causal relationship between events related to the machineries in a process performed on the manufacturing line according to user setting, and a relationship synthesizer that generates a third causal relationship obtained by integrating the first causal relationship and the second causal relationship.

Abstract: A control device includes a trained model for receiving a feature amount calculated from information collected from the control target to output a score, and a threshold value for determining the score An information processing device displays a feature amount related to the trained model and a score calculated from the feature amount, receives setting of a threshold value for any of the feature amount and the score, calculates a threshold value for the score from the threshold value set for the feature amount, and outputs, as a threshold value to be applied to the trained model, any of the threshold value set for the score and the threshold value for the score calculated from the threshold value set for the feature amount.

Abstract: According to the present invention, an axial force of a screw which is generated by screw driving is estimated. A control part applies a predetermined torque generated by a rotation axis servomotor to a screw in a rotation direction where a screw is driven, and thereafter releases the torque. The control part then estimates an axial force of the driven screw based on fluctuation of a rotation position of the rotation axis servomotor which occurs due to the release of the torque.

Abstract: An information processing device including a CPU and a memory storing a program. The CPU generates a plurality of cause-and-effect models between variables based on a relationship between a plurality of mechanisms in a production process of a production line, a dependency between a plurality of events associated with the plurality of mechanisms, or a control-based relationship between the plurality of mechanisms, obtains a result of abnormality detection in the production line, calculates a contribution ratio of a variable contributing to abnormality in the result of abnormality detection, and estimates an abnormality causal factor for at least one of the plurality of cause-and-effect models based on the calculated contribution ratio of the variable contributing to abnormality.

Abstract: A prediction model generator of a prediction system determines as an explanatory variable, one or more status values among status values associated with a training sample to be used for generation of a prediction model, based on an importance with respect to the training sample, determines an interval to be used for prediction by evaluating accuracy of prediction with the determined explanatory variable with an interval included in a search interval being successively varied, and determines a model parameter for defining the prediction model by evaluating plural indicators for the prediction model defined by each model parameter, with the model parameter defining the prediction model being successively varied, under a condition of the determined explanatory variable and the determined interval.

Abstract: A control system includes: a generation unit that generates a dataset for each unit section; a feature extraction unit that generates feature quantity data on the basis of the dataset; and a score calculation unit that calculates a score indicating a degree of deviation of the feature quantity data from learning data, by referring to the learning data. The feature quantity data and the score are output as compression results of the dataset. The control system includes a restoration unit that selects pattern data corresponding to a class determined according to the score contained in the compression result, and after adjusting the pattern data using the feature quantity data contained in the compression results, restores the pattern data as a temporal change in the dataset corresponding to the compression results.

Abstract: A post-processing apparatus includes memory and a processor configured to control a curving device that curves a sheet, which is transported to the curving device, by pressing the sheet, control folding processing that is performed on a sheet curved by the curving device, and control, when folding processing is performed such that two ends of a sheet in a transport direction of the sheet face outward in opposite directions after the folding processing has been performed on the sheet, the curving device so as to curve the sheet in plural directions with respect to a transport surface.

Abstract: An abnormality detection system, support device, and model generation method are provided for generating a more highly accurate abnormality detection model before an actual operation. A model generation part of an abnormality detection system includes a section for generating plural feature values from state values provided from a state value storage part, a section for selecting a combination of one or plural feature values among the plural generated feature values, a section for generating an extra learning data set having at least part of a data series of the feature values of the selected combination and a data series of statistically generated virtual feature values, and a section for evaluating a detection accuracy of a model corresponding to the feature values of the selected combination using the extra learning data set.

Abstract: A post-processing apparatus includes memory and a processor configured to control a curving device that curves a sheet, which is transported to the curving device, by pressing the sheet, control folding processing that is performed on a sheet curved by the curving device, and control, when folding processing is performed such that two ends of a sheet in a transport direction of the sheet face outward in opposite directions after the folding processing has been performed on the sheet, the curving device so as to curve the sheet in plural directions with respect to a transport surface.

Abstract: A prediction model creation apparatus includes a feature amount acquisition unit that acquires values of types of feature amounts that are calculated from operating state data indicating an operating state of a production facility that produces a product, for both a normal time at which the production facility produces the product normally and a defective time at which a defect occurs in the product that is produced, a feature amount selection unit that selects a feature amount effective in predicting the defect from among the acquired types of feature amounts, based on a predetermined algorithm that specifies a degree of association between the defect and the types of feature amounts, from the values of the types of feature amounts acquired at the normal time and the defective time, and a prediction model construction unit that constructs a prediction model for predicting occurrence of the defect, using the selected feature amount.

Abstract: A control system and a control method are provided. A control device in the control system includes a computation processing unit related to control of a control target, a collection unit that executes a process of collecting data associated with the control target, and a monitoring processing unit that executes a process of monitoring a state of the control target and includes a feature quantity generation unit that executes a process for generating a feature quantity from the collected data, and a detection unit that executes a for detecting an abnormality occurring in the control target using the generated feature quantity and an abnormality detection parameter suitable for detection of an abnormality occurring in the control target that is set based on a result of machine learning. An information processing device executes emulation of the monitoring process using the data associated with the control target from the control device.

Abstract: An abnormality detection system, support device, and model generation method for generating a more highly accurate abnormality detection model before an actual operation are provided. A model generation part includes a section for generating feature values from state values provided from a state value storage part; a section for calculating importance levels respectively for the generated feature values based on plural methods, wherein the importance levels indicating a degree that is effective for abnormality detection; and a section for integrating the importance levels calculated based on the plural methods for each of the generated feature values and determining rankings of the importance levels of the generated feature values.

Abstract: There is a need to flexibly set a determination reference suitable for application of predictive maintenance to an actual production site. A first abnormality detection unit includes a calculation unit that calculates a score using a feature quantity that is calculated from a state value related to a monitoring target according to an abnormality detection parameter, and a determination unit that performs a determination using the score calculated by the calculation unit and a first determination reference and a second determination reference included in the abnormality detection parameter, outputs a first determination result when the score matches the first determination reference, and outputs a second determination result when the score matches the second determination reference.

Abstract: An abnormality detection system, support device, and model generation method are provided for generating a more highly accurate abnormality detection model before an actual operation. A model generation part of an abnormality detection system includes a section for generating plural feature values from state values provided from a state value storage part, a section for selecting a combination of one or plural feature values among the plural generated feature values, a section for generating an extra learning data set having at least part of a data series of the feature values of the selected combination and a data series of statistically generated virtual feature values, and a section for evaluating a detection accuracy of a model corresponding to the feature values of the selected combination using the extra learning data set.

Abstract: An abnormality detection system, support device, and model generation method for generating a more highly accurate abnormality detection model before an actual operation are provided. A model generation part includes a section for generating feature values from state values provided from a state value storage part; a section for calculating importance levels respectively for the generated feature values based on plural methods, wherein the importance levels indicating a degree that is effective for abnormality detection; and a section for integrating the importance levels calculated based on the plural methods for each of the generated feature values and determining rankings of the importance levels of the generated feature values.

Abstract: A control system and a control method are provided. A control device in the control system includes a computation processing unit related to control of a control target, a collection unit that executes a process of collecting data associated with the control target, and a monitoring processing unit that executes a process of monitoring a state of the control target and includes a feature quantity generation unit that executes a process for generating a feature quantity from the collected data, and a detection unit that executes a for detecting an abnormality occurring in the control target using the generated feature quantity and an abnormality detection parameter suitable for detection of an abnormality occurring in the control target that is set based on a result of machine learning. An information processing device executes emulation of the monitoring process using the data associated with the control target from the control device.

Abstract: A prediction model creation apparatus includes a feature amount acquisition unit that acquires values of types of feature amounts that are calculated from operating state data indicating an operating state of a production facility that produces a product, for both a normal time at which the production facility produces the product normally and a defective time at which a defect occurs in the product that is produced, a feature amount selection unit that selects a feature amount effective in predicting the defect from among the acquired types of feature amounts, based on a predetermined algorithm that specifies a degree of association between the defect and the types of feature amounts, from the values of the types of feature amounts acquired at the normal time and the defective time, and a prediction model construction unit that constructs a prediction model for predicting occurrence of the defect, using the selected feature amount.

Abstract: A method of generating machining conditions for a numerically controlled machine tool in which a machining is under proper machining conditions without altering the initial machining program even when it has become necessary to change the material of workpiece after completing a machining program. The method comprises the steps of dividing a function for determining machining conditions including a spindle rotational speed and a tool feed speed into a first part of the function independent of the material of a workpiece and a second part of the function dependent on the material of the workpiece.