Abstract: A normal vector registration device includes: a time-series signal clipping processing unit configured to clip a time-series signal in a predetermined period from M (?2) or more types of time-series signals indicating an operation state of a facility during normal operation; a normal vector registration processing unit configured to generate an M-dimensional vector including M types of variables at a same time point from the clipped time-series signal, and register the generated M-dimensional vector at each time point in a database as a normal vector; and a normal vector distribution density leveling unit configured to divide a vector space of the normal vectors, select a predetermined number of normal vectors from the normal vectors included in each divided space to level a distribution density, and optimize a division number of the vector space such that a total number of the selected normal vectors becomes a predesignated number.

Abstract: Provided are an abnormality diagnosis system and an abnormality diagnosis method that can prevent wrongly diagnosing equipment as having an abnormality when the equipment actually does not have an abnormality. An abnormality diagnosis system 20 comprises a sampler 21 and a calculator 24. The calculator 24 is configured to: perform first abnormality determination of whether there is an abnormality based on a result of first principal component analysis; in the case where a result of the first abnormality determination is that there is an abnormality, and perform second abnormality determination of whether there is an abnormality based on a result of second principal component analysis; and in the case where a result of the second abnormality determination is that there is an abnormality, diagnose the equipment as having an abnormality.

Abstract: A process state monitoring device incudes a processor including hardware, the processor being configured to: separate two or more components from time series data of a value indicating a state of a process; compute a characteristic value from each of the separated components; and classify the state of the process on the basis of the computed characteristic values.

Abstract: A method for detecting abnormal vibration of a rolling mill including a collecting step of collecting vibration data of the rolling mill, a frequency analysis step of generating first analysis data by performing frequency analysis of the vibration data, a principal component analysis step of performing principal component analysis on the first analysis data by using reference data specified in advance on the basis of a normal state as a principal component and thereby generating evaluation data, which is a projection of the first analysis data onto the reference data, and an abnormal vibration detection step of extracting an outlier component from the evaluation data and the first analysis data and detecting an abnormality of the rolling mill from the extracted outlier component.

Abstract: An abnormality determination model generating device generates an abnormality determination model for determining an abnormality of a facility performing a predetermined operation, and includes: a time-series signal clipping unit configured to clip K times from one or more time-series signals indicating an operation state of the facility during normal operation of the facility; and an abnormality determination model generating unit configured to generate the abnormality determination model from the time-series signals during the normal operation clipped out by the time-series signal clipping unit, wherein the abnormality determination model generating unit is configured to clip L items per one time of clipping from the time-series signals during the normal operation clipped by the time-series signal clipping unit and configures an L-dimensional vector including L variables.

Abstract: A breakout prediction method includes: a step of inputting a dimension of a solid product withdrawn from a mold in a continuous casting machine; a step of detecting a temperature of the mold by a plurality of thermometers embedded in the mold; a step of executing interpolation processing on the detected temperatures detected by the plurality of thermometers according to the dimension of the solid product; a step of calculating, based on the temperatures calculated by executing the interpolation processing, a component in a direction orthogonal to an influence coefficient vector obtained by principal component analysis as a degree of deviation from during a normal operation in which a breakout has not occurred; and a step of predicting a breakout based on the degree of deviation.

Abstract: A method for detecting abnormal vibration of a rolling mill including a collecting step of collecting vibration data of the rolling mill, a frequency analysis step of generating first analysis data indicative of vibration intensities at respective frequencies by performing frequency analysis of the vibration data, a data conversion step of converting the first analysis data into second analysis data indicative of vibration intensities at respective intervals on the basis of a rolling speed, and a map generation step of generating a vibration map in which a plurality of the second analysis data are arranged in time series.

Abstract: A trigger condition determination method for a time series signal determines a trigger condition for cutting out a monitored section being a target for abnormality diagnosis, from a monitored signal being a time series signal indicating a condition of a monitored facility in the abnormality diagnosis for the monitored facility, and includes: collecting signal groups including one or more monitored signals and a trigger candidate signal; cutting out the monitored section of the monitored signal; generating a learning model specifying a start time point of the cut-out monitored section, generating label data, and using one or more trigger candidate signals at each time point as an input and using the label data at each time point as an output, by using machine learning; and determining the trigger condition by using the learning model, for the monitored signal for which the abnormality diagnosis is performed.

Abstract: An abnormality determination apparatus: performs, during normal operation of the facility, K times of clipping from time-series signals indicating an operation state of the facility; sets M types as types of the time-series signals clipped by the time-series signal clipping unit, constructs an M-dimensional vector, and registers the constructed vector as a normal vector; sets an abnormality determination flag as a first type when a maximum value of correlation between variables is less than a predetermined value, sets an abnormality determination flag as a second type when the maximum value is the predetermined value or more, and performs, when the flag is of the second type, a principal component analysis on a registered normal vector group to calculate a transform coefficient of a principal component and registers each of the calculated transform coefficients as an abnormality determination model; and determines an abnormality of the facility.

Abstract: A construction method of an abnormality diagnosis model of a process for sequentially treating a metal material in a plurality of facilities, the construction method includes: creating a first abnormality diagnosis model that learns a relationship between measured values at a same time and an abnormality by using the measured values measured at the same time in a predetermined measurement cycle determined in advance for the plurality of facilities; and creating a second abnormality diagnosis model that learns a relationship between measured values at a same position and an abnormality by using the measured values at the same position of the metal material obtained by compiling the measured values measured in the plurality of facilities for each position of the metal material.

Abstract: A construction method of an abnormality diagnosis model for diagnosing an abnormality of a process, the construction method includes: creating a first regression model that sets a regression coefficient regarding an explanatory variable with a small influence on a response variable to zero by using all operational data in normal times collected in advance; dividing the operational data into a plurality of segments determined in advance and determining an explanatory variable candidate for each of the segments within a range of the explanatory variable used in the first regression model; and creating a second regression model that sets a regression coefficient regarding an explanatory variable candidate with a small influence on a response variable to zero by using the operational data included in the segment.

Abstract: A breakout prediction method includes: a step of inputting a dimension of a solid product withdrawn from a mold in a continuous casting machine; a step of detecting a temperature of the mold by a plurality of thermometers embedded in the mold; a step of executing interpolation processing on the detected temperatures detected by the plurality of thermometers according to the dimension of the solid product; a step of calculating, based on the temperatures calculated by executing the interpolation processing, a component in a direction orthogonal to an influence coefficient vector obtained by principal component analysis as a degree of deviation from during a normal operation in which a breakout has not occurred; and a step of predicting a breakout based on the degree of deviation.

Abstract: Provided are an abnormality diagnosis system and an abnormality diagnosis method that can prevent wrongly diagnosing equipment as having an abnormality when the equipment actually does not have an abnormality. An abnormality diagnosis system 20 comprises a sampler 21 and a calculator 24. The calculator 24 is configured to: perform first abnormality determination of whether there is an abnormality based on a result of first principal component analysis; in the case where a result of the first abnormality determination is that there is an abnormality, and perform second abnormality determination of whether there is an abnormality based on a result of second principal component analysis; and in the case where a result of the second abnormality determination is that there is an abnormality, diagnose the equipment as having an abnormality.

Abstract: A fine ratio measuring device that measures the fine ratio of fines adhering to the surface of the material in the form of lumps includes: an illumination unit that illuminates the material in the form of lumps; an imaging unit that captures an image of the material in the form of lumps and produces image data; and an arithmetic unit including a computation unit that computes a characteristic quantity of the image data produced by the imaging unit and a conversion unit that converts the characteristic quantity computed by the computation unit to the fine ratio.

Abstract: Provided are a raw material particle size distribution measuring apparatus and a particle size distribution measuring method. Also provided is a porosity measuring apparatus. The raw material particle size distribution measuring apparatus includes: a coarse particle measuring device that acquires information indicating the particle size distribution of the coarse particles; a fine particle measuring device that acquires information indicating the particle size distribution of the fine particles; and an arithmetic device that computes the particle size distribution of the coarse particles using the information indicating the particle size distribution of the coarse particles, computes the particle size distribution of the fine particles using the information indicating the particle size distribution of the fine particles, and computes an overall particle size distribution of the raw material using the particle size distribution of the coarse particles and the particle size distribution of the fine particles.

Abstract: A process state monitoring device incudes a processor including hardware, the processor being configured to: separate two or more components from time series data of a value indicating a state of a process; compute a characteristic value from each of the separated components; and classify the state of the process on the basis of the computed characteristic values.

Abstract: A process anomalous state diagnostic device configured to diagnose an anomalous state of a process based on deviation indexes for the magnitude of deviation from a reference that is a normal state of the process includes: a color mapping unit configured to configure a two-dimensional matrix that has a first axis as an axis of a temporal factor including time and that has a second axis as an axis of an item of each deviation index, associate each cell of the matrix with data for an item of the deviation index and the temporal factor, and allocate a color in accordance with the magnitude of the deviation index to each cell of the matrix; and a color map display unit configured to display a color map produced by the color mapping unit.

Abstract: Provided are a raw material particle size distribution measuring apparatus and a particle size distribution measuring method. Also provided is a porosity measuring apparatus. The raw material particle size distribution measuring apparatus includes: a coarse particle measuring device that acquires information indicating the particle size distribution of the coarse particles; a fine particle measuring device that acquires information indicating the particle size distribution of the fine particles; and an arithmetic device that computes the particle size distribution of the coarse particles using the information indicating the particle size distribution of the coarse particles, computes the particle size distribution of the fine particles using the information indicating the particle size distribution of the fine particles, and computes an overall particle size distribution of the raw material using the particle size distribution of the coarse particles and the particle size distribution of the fine particles.