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公开(公告)号：US20240027304A1
公开(公告)日：2024-01-25
申请号：US18039799
申请日：2020-12-08
Applicant: JFE STEEL CORPORATION
Inventor： Kei SHOMURA , Takehide HIRATA
CPC classification number: G01M99/005 , G06N20/00
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.
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公开(公告)号：US20250020547A1
公开(公告)日：2025-01-16
申请号：US18714705
申请日：2022-12-19
Applicant: JFE STEEL CORPORATION
Inventor： Takehide HIRATA , Masafumi MATSUSHITA
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.
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公开(公告)号：US20240033799A1
公开(公告)日：2024-02-01
申请号：US18283994
申请日：2022-02-04
Applicant: JFE STEEL CORPORATION
Inventor： Wataru BABA , Masahide YAJIMA , Takehide HIRATA , Yukio TAKASHIMA
CPC classification number: B21B38/008 , B21C51/00
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.
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公开(公告)号：US20230393113A1
公开(公告)日：2023-12-07
申请号：US18032239
申请日：2021-09-21
Applicant: JFE STEEL CORPORATION
Inventor： Masafumi MATSUSHITA , Takehide HIRATA
IPC: G01N33/20
CPC classification number: G01N33/20
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.
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公开(公告)号：US20230384780A1
公开(公告)日：2023-11-30
申请号：US18031656
申请日：2021-09-21
Applicant: JFE STEEL CORPORATION
Inventor： Masafumi MATSUSHITA , Takehide HIRATA , Akira KUMANO
IPC: G05B23/02
CPC classification number: G05B23/0243 , G06Q10/06395
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.
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Patent Agency Ranking
公开(公告)号：US20230024947A1
公开(公告)日：2023-01-26
申请号：US17758358
申请日：2020-12-21
Applicant: JFE STEEL CORPORATION
Inventor： Tetsushi NAGANO , Takehide HIRATA
IPC: G05B23/02
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.
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公开(公告)号：US20240165685A1
公开(公告)日：2024-05-23
申请号：US18283958
申请日：2022-02-04
Applicant: JFE STEEL CORPORATION
Inventor： Wataru BABA , Masahide YAJIMA , Takehide HIRATA , Yukio TAKASHIMA
CPC classification number: B21B38/008 , G01H1/003
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.
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公开(公告)号：US20240110850A1
公开(公告)日：2024-04-04
申请号：US18265353
申请日：2021-10-04
Applicant: JFE STEEL CORPORATION
Inventor： Takehide HIRATA , Kei SHOMURA
IPC: G01M99/00
CPC classification number: G01M99/005
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.
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9.发明公开公开(公告)号：US20240004378A1
公开(公告)日：2024-01-04
申请号：US18039638
申请日：2021-10-04
Applicant: JFE STEEL CORPORATION
Inventor： Takehide HIRATA , Kei SHOMURA
IPC: G05B23/02
CPC classification number: G05B23/0221 , G05B23/024
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.
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10.发明公开公开(公告)号：US20230226600A1
公开(公告)日：2023-07-20
申请号：US18009909
申请日：2021-04-09
Applicant: JFE STEEL CORPORATION
Inventor： Ryosuke MASUDA , Yoshinari HASHIMOTO , Akitoshi MATSUI , Shugo MORITA , Tatsuro HAYASHIDA , Taiga KORIYAMA , Takehide HIRATA
CPC classification number: B22D11/182 , B22D11/04 , B22D2/00
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.
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