公开(公告)号：US20220152830A1

公开(公告)日：2022-05-19

申请号：US17525960

申请日：2021-11-15

Applicant: KABUSHIKI KAISHA YASKAWA DENKI

Inventor： Ryo KABUTAN ,  Takuro MATSUMOTO ,  Ryohei SUZUKI

IPC: B25J9/16 ,  G01M99/00 ,  B23K37/02

Abstract: An example diagnosis system determines a state of a target device that includes a work apparatus. The diagnosis system includes circuitry that is configured to acquire first data generated in response to operating the work apparatus at a first pressure, configured to acquire second data generated in response to operating the work apparatus at a second pressure, configured to calculate a feature quantity indicating a relation between the first data and the second data, and configured to determine the state of the target device based on the feature quantity.

公开(公告)号：US20240152105A1

公开(公告)日：2024-05-09

申请号：US18416887

申请日：2024-01-19

Applicant: KABUSHIKI KAISHA YASKAWA DENKI

Inventor： Ryo KABUTAN ,  Ryohei SUZUKI ,  Takeshi UEDA ,  Masayuki MORI

IPC: G05B19/042

CPC classification number: G05B19/042 ,  G05B2219/49061

Abstract: An operation adjustment system includes estimation circuitry and generation circuitry. The estimation circuitry is configured to generate a calculation model based on a plurality of pairs of a parameter set and an evaluation index. The calculation model indicates a relationship between the parameter set and the evaluation index. The parameter set affects an operation of a motor control device. The evaluation index relates to a machine operated according to the parameter set by the motor control device. The generation circuitry is configured to generate a new parameter set based on the calculation model in order to update the calculation model with the new parameter set.

公开(公告)号：US20220342405A1

公开(公告)日：2022-10-27

申请号：US17714997

申请日：2022-04-06

Applicant: KABUSHIKI KAISHA YASKAWA DENKI

Inventor： Ryohei SUZUKI ,  Marino KASHIYAMA

IPC: G05B23/02

Abstract: An abnormality detection apparatus is provided, comprising a target data generation unit configured to generate, based on operation-related data resulting from an operation of a movable apparatus, a plurality of target data that are temporally separated, and a detection processing execution unit configured to execute change detection processing on the plurality of target data. An abnormality detection method that is executed by a computer for detecting an abnormality in a movable apparatus is provided, the method comprising generating, based on operation-related data resulting from an operation of the movable apparatus, a plurality of target data that are temporally separated, and executing change detection processing on the plurality of target data.

公开(公告)号：US20240144101A1

公开(公告)日：2024-05-02

申请号：US18496912

申请日：2023-10-29

Applicant: KABUSHIKI KAISHA YASKAWA DENKI

Inventor： Ryohei SUZUKI ,  Ryo KABUTAN ,  Takuya KADOYA

IPC: G06N20/00

CPC classification number: G06N20/00

Abstract: Provided is a generation system including: one or more processors which acquire waveform data; specify an intention of a user; and generate pseudo waveform data from the waveform data acquired in such a manner that an intention of a user specified is reflected. Provided is a method for generating a waveform evaluation model executed by a computer, including: acquiring waveform data; specifying an intention of a user; generating pseudo waveform by generating pseudo waveform data from the waveform data acquired in the acquiring the waveform data in such a manner that the intention of the user specified in the specifying the intention is reflected; and executing learning by executing machine learning using the pseudo waveform data generated in the generating the pseudo waveform to generate a waveform evaluation model which outputs an evaluation result of input waveform data.

公开(公告)号：US20240051117A1

公开(公告)日：2024-02-15

申请号：US18354653

申请日：2023-07-19

Applicant: KABUSHIKI KAISHA YASKAWA DENKI

Inventor： Ryo KABUTAN ,  Ryohei SUZUKI ,  Takuya KADOYA

IPC: B25J9/00 ,  B25J9/16 ,  B65G1/00 ,  B25J13/08

CPC classification number: B25J9/0093 ,  B25J9/1653 ,  B25J9/1697 ,  B25J9/1656 ,  B65G1/00 ,  B25J13/088 ,  B25J9/126

Abstract: A robot system includes circuitry and a robot. The circuitry is configured to set, based on a plurality of workpieces that move along a conveying surface, a window corresponding to n workpieces of the plurality of workpieces, according to an inflow order that is an order in which the plurality of workpieces have moved along the conveying surface, where n>1. The circuitry is further configured to determine a picking order by an optimization algorithm, based on a position of each of the n workpieces. The picking order is an order of picking one or more of the workpieces from the conveying surface and is different from the inflow order. The circuitry is further configured to select m workpieces from the window based on the picking order, where n>m. The robot is configured to pick the selected m workpieces from the conveying surface in the picking order.

公开(公告)号：US20230134186A1

公开(公告)日：2023-05-04

申请号：US18088766

申请日：2022-12-26

Applicant: Kabushiki Kaisha Yaskawa Denki

Inventor： Ryohei SUZUKI ,  Tsuyoshi Yokoya ,  Ryo Masumura ,  Hiroki Tachikake

IPC: G06N3/094

Abstract: Provided a machine learning data generation device including: at least one processor; and at least one memory device that stores a plurality of instructions which, when executed by the at least one processor, causes the at least one processor to execute: acquiring , in association with a predetermined label actual time series information; executing physical simulation of generating a plurality of pieces of virtual time series information; identifying parameter values based on the plurality of pieces of virtual time series information and the actual time series information, and to associate the identified parameter values with the label; generating a new parameter value and the label based on the identified parameter values; generating virtual time series information corresponding to a new internal state by executing physical simulation through use of the new parameter value; and generating new machine learning data.