SG10201903974UA - Method and system for accelerating convergence of recurrent neural network for machine failure prediction - Google Patents

Method and system for accelerating convergence of recurrent neural network for machine failure prediction

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Publication number
SG10201903974UA
SG10201903974UA SG10201903974UA SG10201903974UA SG10201903974UA SG 10201903974U A SG10201903974U A SG 10201903974UA SG 10201903974U A SG10201903974U A SG 10201903974UA SG 10201903974U A SG10201903974U A SG 10201903974UA SG 10201903974U A SG10201903974U A SG 10201903974UA
Authority
SG
Singapore
Prior art keywords
neural network
recurrent neural
failure prediction
machine failure
accelerating convergence
Prior art date
Application number
SG10201903974UA
Inventor
Bhandary Chiranjib
Original Assignee
Avanseus Holdings Pte Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Avanseus Holdings Pte Ltd filed Critical Avanseus Holdings Pte Ltd
Publication of SG10201903974UA publication Critical patent/SG10201903974UA/en

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Classifications

    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00—Testing or monitoring of control systems or parts thereof
    • G05B23/02—Electric testing or monitoring
    • G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
    • G05B23/0224—Process history based detection method, e.g. whereby history implies the availability of large amounts of data
    • G05B23/024—Quantitative history assessment, e.g. mathematical relationships between available data; Functions therefor; Principal component analysis [PCA]; Partial least square [PLS]; Statistical classifiers, e.g. Bayesian networks, linear regression or correlation analysis; Neural networks
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00—Testing or monitoring of control systems or parts thereof
    • G05B23/02—Electric testing or monitoring
    • G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/044—Recurrent networks, e.g. Hopfield networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/086—Learning methods using evolutionary algorithms, e.g. genetic algorithms or genetic programming
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00—Testing or monitoring of control systems or parts thereof
    • G05B23/02—Electric testing or monitoring
    • G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G05B23/0259—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterized by the response to fault detection
    • G05B23/0283—Predictive maintenance, e.g. involving the monitoring of a system and, based on the monitoring results, taking decisions on the maintenance schedule of the monitored system; Estimating remaining useful life [RUL]

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Artificial Intelligence (AREA)
  • Evolutionary Computation (AREA)
  • Mathematical Physics (AREA)
  • Software Systems (AREA)
  • Automation & Control Theory (AREA)
  • Data Mining & Analysis (AREA)
  • Computing Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Health & Medical Sciences (AREA)
  • Biophysics (AREA)
  • Molecular Biology (AREA)
  • General Health & Medical Sciences (AREA)
  • Computational Linguistics (AREA)
  • Biomedical Technology (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Medical Informatics (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Evolutionary Biology (AREA)
  • Physiology (AREA)
  • Debugging And Monitoring (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
SG10201903974UA 2019-04-06 2019-05-03 Method and system for accelerating convergence of recurrent neural network for machine failure prediction SG10201903974UA (en)

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
IN201911013965 2019-04-06

Publications (1)

Publication Number Publication Date
SG10201903974UA true SG10201903974UA (en) 2020-11-27

Family

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Family Applications (1)

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SG10201903974UA SG10201903974UA (en) 2019-04-06 2019-05-03 Method and system for accelerating convergence of recurrent neural network for machine failure prediction

Country Status (2)

Country Link
US (1) US11099552B2 (en)
SG (1) SG10201903974UA (en)

Families Citing this family (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20220129746A1 (en) * 2020-10-27 2022-04-28 International Business Machines Corporation Decentralized parallel min/max optimization
CN113556397B (en) * 2021-07-21 2022-05-06 山东建筑大学 Cloud service resource scheduling method for IoT gateway
CN113904948B (en) * 2021-11-12 2023-11-03 福州大学 5G network bandwidth prediction system and method based on cross-layer multidimensional parameters
CN114708924B (en) * 2022-03-28 2024-06-18 大唐环境产业集团股份有限公司 Model construction method and device for predicting soot blower soot blowing interval time in SCR system

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112602156A (en) * 2018-02-27 2021-04-02 康奈尔大学 System and method for detecting residual disease

Also Published As

Publication number Publication date
US20200319631A1 (en) 2020-10-08
US11099552B2 (en) 2021-08-24

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