公开(公告)号：US20240231353A9

公开(公告)日：2024-07-11

申请号：US18455146

申请日：2023-08-24

Applicant: KABUSHIKI KAISHA TOSHIBA ,  TOSHIBA ENERGY SYSTEMS & SOLUTIONS CORPORATION

Inventor： Naoyuki TAKADO ,  Chikashi MIYAMOTO ,  Toshio AOKI ,  Shinya TOMINAGA ,  Ryota MIYAKE

IPC: G05B23/02

CPC classification number: G05B23/0283

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.

公开(公告)号：US20240214086A1

公开(公告)日：2024-06-27

申请号：US18240175

申请日：2023-08-30

Applicant: Strong Force IoT Portfolio 2016, LLC

Inventor： Charles Howard Cella ,  Gerald William Duffy, JR. ,  Jeffrey P. McGuckin ,  Mehul Desai

IPC: H04B17/29 ,  B62D5/04 ,  B62D15/02 ,  G01M13/028 ,  G01M13/04 ,  G01M13/045 ,  G05B13/02 ,  G05B19/042 ,  G05B19/418 ,  G05B23/02 ,  G06F17/18 ,  G06F18/21 ,  G06F18/25 ,  G06N3/006 ,  G06N3/02 ,  G06N3/044 ,  G06N3/045 ,  G06N3/047 ,  G06N3/084 ,  G06N3/088 ,  G06N3/126 ,  G06N5/046 ,  G06N7/01 ,  G06N20/00 ,  G06Q10/04 ,  G06Q10/0639 ,  G06Q30/02 ,  G06Q30/06 ,  G06Q50/00 ,  G06V10/778 ,  G06V10/82 ,  G16Z99/00 ,  H02M1/12 ,  H03M1/12 ,  H04B17/23 ,  H04B17/309 ,  H04B17/318 ,  H04B17/345 ,  H04B17/40 ,  H04L1/00 ,  H04L1/18 ,  H04L1/1867 ,  H04L5/00 ,  H04L67/1097 ,  H04L67/12 ,  H04L67/306 ,  H04W4/38 ,  H04W4/70

CPC classification number: H04B17/29 ,  B62D15/0215 ,  G01M13/028 ,  G01M13/04 ,  G01M13/045 ,  G05B13/028 ,  G05B19/4183 ,  G05B19/4184 ,  G05B19/41845 ,  G05B19/4185 ,  G05B19/41865 ,  G05B19/41875 ,  G05B23/0221 ,  G05B23/0229 ,  G05B23/024 ,  G05B23/0264 ,  G05B23/0283 ,  G05B23/0286 ,  G05B23/0289 ,  G05B23/0291 ,  G05B23/0294 ,  G05B23/0297 ,  G06F18/2178 ,  G06N3/006 ,  G06N3/02 ,  G06N3/044 ,  G06N3/045 ,  G06N3/047 ,  G06N3/084 ,  G06N3/088 ,  G06N5/046 ,  G06N7/01 ,  G06N20/00 ,  G06Q10/04 ,  G06Q10/0639 ,  G06Q30/02 ,  G06Q30/0278 ,  G06Q30/06 ,  G06Q50/00 ,  G06V10/7784 ,  G06V10/82 ,  G16Z99/00 ,  H02M1/12 ,  H03M1/12 ,  H04B17/23 ,  H04B17/309 ,  H04B17/318 ,  H04B17/345 ,  H04L1/0002 ,  H04L1/0041 ,  H04L1/18 ,  H04L1/1874 ,  H04L67/1097 ,  H04L67/12 ,  H04W4/38 ,  H04W4/70 ,  B62D5/0463 ,  G05B19/042 ,  G05B23/02 ,  G05B23/0208 ,  G05B2219/32287 ,  G05B2219/35001 ,  G05B2219/37337 ,  G05B2219/37351 ,  G05B2219/37434 ,  G05B2219/37537 ,  G05B2219/40115 ,  G05B2219/45004 ,  G05B2219/45129 ,  G06F17/18 ,  G06F18/21 ,  G06F18/217 ,  G06F18/25 ,  G06N3/126 ,  H04B17/40 ,  H04L1/0009 ,  H04L5/0064 ,  H04L67/306 ,  Y02P80/10 ,  Y02P90/02 ,  Y02P90/80 ,  Y04S50/00 ,  Y04S50/12 ,  Y10S707/99939

Abstract: In some embodiments, a monitoring system for an industrial environment includes a data collector structured to collect data from at least one of a plurality of sensors, an expert system configured to analyze the collected data and generate a corresponding heat map, and a heat map interface to provide the heat map to an AR/VR device, wherein the heat map overlays a view of the underlying sensors, and wherein the data collector is further configured to collect user data, representative of a behavior of the user, from the AR/VR device.

公开(公告)号：US20240210935A1

公开(公告)日：2024-06-27

申请号：US18087633

申请日：2022-12-22

Applicant: Delaware Capital Formation, Inc.

Inventor： Bodhayan Dev ,  Prem Swaroop ,  Richard Buteau ,  Girish Juneja ,  Sreedhar Patnala

IPC: G05B23/02

CPC classification number: G05B23/0283 ,  G05B23/0216 ,  G05B23/024

Abstract: Among other things, systems and techniques are described for a predictive model for determining overall equipment effectiveness (OEE) in industrial equipment. Data including spectral features is obtained. A probability of survival is determined by fitting at least one degradation function to degradation data associated with the industrial equipment. An overall equipment effectiveness metric is predicted as a product of predicted planned production time, predicted performance, and predicted quality output by trained machine learning models.

公开(公告)号：US12007759B2

公开(公告)日：2024-06-11

申请号：US17361189

申请日：2021-06-28

Applicant: Oracle International Corporation

Inventor： Dieter Gawlick ,  Matthew Torin Gerdes ,  Kirk Bradley ,  Anna Chystiakova ,  Zhen Hua Liu ,  Guang Chao Wang ,  Kenny C. Gross

IPC: G05B23/02 ,  G06F11/34 ,  G06F16/215 ,  G06F18/214 ,  G06N20/00

CPC classification number: G05B23/0283 ,  G06F11/3409 ,  G06F16/215 ,  G06F18/2148 ,  G06N20/00

Abstract: Techniques for geometric aging data reduction for machine learning applications are disclosed. In some embodiments, an artificial-intelligence powered system receives a first time-series dataset that tracks at least one metric value over time. The system then generates a second time-series dataset that includes a reduced version of a first portion of the time-series dataset and a non-reduced version of a second portion of the time-series dataset. The second portion of the time-series dataset may include metric values that are more recent than the first portion of the time-series dataset. The system further trains a machine learning model using the second time-series dataset that includes the reduced version of the first portion of the time-series dataset and the non-reduced version of the second portion of the time-series dataset. The trained model may be applied to reduced and/or non-reduced data to detect multivariate anomalies and/or provide other analytic insights.

公开(公告)号：US11983000B2

公开(公告)日：2024-05-14

申请号：US17313032

申请日：2021-05-06

Applicant: SKF LUBRICATION SYSTEMS GERMANY GMBH

Inventor： Stefan Gebauer ,  Armin Guenther ,  Dieter Hess ,  Juergen Kreutzkaemper ,  Andreas Stellmach

IPC: G05B23/02 ,  F16N7/38 ,  F16N29/02

CPC classification number: G05B23/0283 ,  F16N7/385 ,  F16N29/02 ,  G05B23/0224 ,  G05B23/0264 ,  G05B23/0267

Abstract: A device for outputting a future state of a central lubrication system includes at least one sensor for recording a parameter of the central lubrication system, a processing unit for processing the recorded parameter, determining a current state of the central lubrication system based on the processed parameter, and estimating a future state of the central lubrication system over a certain period of time based on the current state and stored data, and an output unit for outputting the future state of the central lubrication system.

公开(公告)号：US20240126252A1

公开(公告)日：2024-04-18

申请号：US18083583

申请日：2022-12-19

Applicant: Wistron Corporation

Inventor： Chun-Hsien Li ,  Chia-Chiung Liu

IPC: G05B23/02

CPC classification number: G05B23/0272 ,  G05B23/024 ,  G05B23/0283

Abstract: The disclosure provides a method for suggesting equipment maintenance, an electronic device, and a computer readable recording medium. Equipment operation information of equipment is obtained. An energy efficiency of the equipment is determined according to the equipment operation information. Status difference data of the equipment is generated according to the equipment operation information in response to the energy efficiency meeting a maintenance condition. At least one maintenance item corresponding to the equipment is determined according to the status difference data. Suggestion information of the at least one maintenance item is provided through a display.

公开(公告)号：US11954954B2

公开(公告)日：2024-04-09

申请号：US16661553

申请日：2019-10-23

Applicant: Airbus SAS ,  Airbus Operations SAS

Inventor： Alain Lagarrigue ,  David Cumer ,  Subodh Kumar Keshri

IPC: G07C5/08 ,  B64D45/00 ,  G05B23/02

CPC classification number: G07C5/0816 ,  B64D45/00 ,  G05B23/0283 ,  G07C5/0808 ,  G07C5/085 ,  B64D2045/0065 ,  B64D2045/0085

Abstract: To reduce the bulk of the acquisition systems embedded onboard an aircraft and dedicated to predicting failures, an acquisition system is provided comprising an avionics rack and at least one recording device, in which the avionics rack comprises a front panel provided with at least one test connector configured to be connected to all or some of the data buses of the aircraft, each recording device comprises a housing and an acquisition port, the acquisition port being provided on an outer face of the housing, the acquisition port of each recording device is engaged with a corresponding test connector of the avionics rack, each recording device is configured to acquire at least some signals applied to the acquisition port by the corresponding test connector, and to store the acquired signals.

公开(公告)号：US20240112031A1

公开(公告)日：2024-04-04

申请号：US18531988

申请日：2023-12-07

Applicant: ACOEM France

Inventor： Bertrand WASCAT ,  Philippe POIZAT ,  Jean Michel BECU

IPC: G06N3/08 ,  G05B23/02 ,  G06N3/047

CPC classification number: G06N3/08 ,  G05B23/0221 ,  G05B23/0272 ,  G05B23/0283 ,  G06N3/047

Abstract: Supervised learning is implemented to improve the accuracy of automated diagnoses performed by monitoring units installed at a machine. The monitoring units perform indicator acquisition and automated diagnoses based on a Bayesian model derived in accordance with the machine's known configuration. Raw data is collected, including machine vibration data and other diagnostic data. The data is analyzed to diagnose for specific fault defect assumptions so as to generate the automated diagnoses results and a rating for overall health of the machine. The results are uploaded to an external environment that can be accessed by an expert for review and correction. Based upon the expert's corrections, the Bayesian model is adjusted using supervised learning to improve the automated diagnoses performed by the monitoring units.

公开(公告)号：US11941521B2

公开(公告)日：2024-03-26

申请号：US17017912

申请日：2020-09-11

Applicant: ACOEM France

Inventor： Bertrand Wascat ,  Philippe Poizat ,  Jean Michel Becu

IPC: G06N3/08 ,  G05B23/02 ,  G06N3/047

CPC classification number: G06N3/08 ,  G05B23/0221 ,  G05B23/0272 ,  G05B23/0283 ,  G06N3/047

Abstract: Supervised learning is implemented to improve the accuracy of automated diagnoses performed by monitoring units installed at a machine. The monitoring units perform indicator acquisition and automated diagnoses based on a Bayesian model derived in accordance with the machine's known configuration. Raw data is collected, including machine vibration data and other diagnostic data. The data is analyzed to diagnose for specific fault defect assumptions so as to generate the automated diagnoses results and a rating for overall health of the machine. The results are uploaded to an external environment that can be accessed by an expert for review and correction. Based upon the expert's corrections, the Bayesian model is adjusted using supervised learning to improve the automated diagnoses performed by the monitoring units.

公开(公告)号：US11940785B2

公开(公告)日：2024-03-26

申请号：US17498999

申请日：2021-10-12

Applicant: ASM IP Holding B.V.

Inventor： Taku Omori

IPC: G05B23/02 ,  H01L21/67

CPC classification number: G05B23/0283 ,  G05B23/0235 ,  G05B23/0272 ,  H01L21/67017 ,  H01L21/67253 ,  H01L21/67011

Abstract: Examples of a predictive maintenance method includes determining whether analog data measured in a substrate treatment that has used a recipe exceeds an allowable threshold which corresponds to the recipe and has been determined beforehand, and notifying, in a case where it is determined that the analog data exceeds the allowable threshold in the determination, a user that a relating module which has been associated with the analog data beforehand has deteriorated.