SG10201610116PA - Method and system for machine failure prediction - Google Patents

Method and system for machine failure prediction

Info

Publication number
SG10201610116PA
SG10201610116PA SG10201610116PA SG10201610116PA SG10201610116PA SG 10201610116P A SG10201610116P A SG 10201610116PA SG 10201610116P A SG10201610116P A SG 10201610116PA SG 10201610116P A SG10201610116P A SG 10201610116PA SG 10201610116P A SG10201610116P A SG 10201610116PA
Authority
SG
Singapore
Prior art keywords
weight range
basic
memory depth
machine failure
basic memory
Prior art date
Application number
SG10201610116PA
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 SG10201610116PA publication Critical patent/SG10201610116PA/en

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Classifications

    • 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
    • G06N3/084—Backpropagation, e.g. using gradient descent
    • 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
    • G05B23/0243—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 model based detection method, e.g. first-principles knowledge model
    • G05B23/0254—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 model based detection method, e.g. first-principles knowledge model based on a quantitative model, e.g. mathematical relationships between inputs and outputs; functions: observer, Kalman filter, residual calculation, Neural 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
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/044—Recurrent networks, e.g. Hopfield networks

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Artificial Intelligence (AREA)
  • Evolutionary Computation (AREA)
  • Mathematical Physics (AREA)
  • General Physics & Mathematics (AREA)
  • Biophysics (AREA)
  • Molecular Biology (AREA)
  • Biomedical Technology (AREA)
  • Health & Medical Sciences (AREA)
  • Computational Linguistics (AREA)
  • Data Mining & Analysis (AREA)
  • General Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Computing Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Software Systems (AREA)
  • Automation & Control Theory (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
  • Debugging And Monitoring (AREA)
  • Testing And Monitoring For Control Systems (AREA)

Abstract

METHOD AND SYSTEM FOR MACHINE FAILURE PREDICTION Embodiments of the invention provide a method and system for machine failure prediction. The method comprises: identifying a plurality of basic memory depth values based on a machine failure history; ascertaining a basic weight range for each of the plurality of basic memory depth values according to a pre-stored table including a plurality of mappings each mapping between a basic memory depth value and a basic weight range, or a predetermined formula for calculating the basic weight range based on the corresponding basic memory depth value; 10 ascertaining a composite initial weight range by calculating an average weight range of the ascertained basic weight range for each identified basic memory depth value; generating initial weights based on the composite initial weight range; and predicting a future failure using a Back Propagation Through Time (BPTT) trained Recurrent Neural Network (RNN) based on the generated initial 15 weights. (Figure 3) 20 32
SG10201610116PA 2016-11-03 2016-12-02 Method and system for machine failure prediction SG10201610116PA (en)

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
IN201611037626 2016-11-03

Publications (1)

Publication Number Publication Date
SG10201610116PA true SG10201610116PA (en) 2018-06-28

Family

ID=62021594

Family Applications (1)

Application Number Title Priority Date Filing Date
SG10201610116PA SG10201610116PA (en) 2016-11-03 2016-12-02 Method and system for machine failure prediction

Country Status (2)

Country Link
US (1) US10909458B2 (en)
SG (1) SG10201610116PA (en)

Families Citing this family (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US11494654B2 (en) * 2016-11-03 2022-11-08 Avanseus Holdings Pte. Ltd. Method for machine failure prediction using memory depth values
JP7221644B2 (en) * 2018-10-18 2023-02-14 株式会社日立製作所 Equipment failure diagnosis support system and equipment failure diagnosis support method
CN109583124B (en) * 2018-12-13 2023-02-03 北京计算机技术及应用研究所 HMM fault prediction system based on ADRC
CN111538914B (en) * 2019-02-01 2023-05-30 阿里巴巴集团控股有限公司 Address information processing method and device
WO2020193330A1 (en) * 2019-03-23 2020-10-01 British Telecommunications Public Limited Company Automated device maintenance
CN109978275B (en) * 2019-04-03 2021-03-12 中南大学 Extreme strong wind speed prediction method and system based on mixed CFD and deep learning
CN110186570B (en) * 2019-05-16 2021-01-15 西安理工大学 Additive manufacturing laser 3D printing temperature gradient detection method
CN113950707A (en) 2019-06-10 2022-01-18 皇家飞利浦有限公司 System and method for predicting part replacement dependencies based on heterogeneous subsystem analysis
CN110817694B (en) * 2019-10-25 2020-08-28 湖南中联重科智能技术有限公司 Load hoisting weight calculation method and device and storage medium
US11763160B2 (en) * 2020-01-16 2023-09-19 Avanseus Holdings Pte. Ltd. Machine learning method and system for solving a prediction problem
CN111476347B (en) * 2020-03-04 2023-03-24 国网安徽省电力有限公司检修分公司 Maintenance method, system and storage medium of phase modulator based on multiple factors

Family Cites Families (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5448681A (en) * 1992-03-27 1995-09-05 National Semiconductor Corporation Intelligent controller with neural network and reinforcement learning
US6708160B1 (en) * 1999-04-06 2004-03-16 Paul J. Werbos Object nets
US20030065525A1 (en) * 2001-10-01 2003-04-03 Daniella Giacchetti Systems and methods for providing beauty guidance
US20180046151A1 (en) * 2015-03-11 2018-02-15 Siemens Indsutry, Inc. Cascaded identification in building automation
US9336482B1 (en) * 2015-07-27 2016-05-10 Google Inc. Predicting likelihoods of conditions being satisfied using recurrent neural networks
US10387768B2 (en) * 2016-08-09 2019-08-20 Palo Alto Research Center Incorporated Enhanced restricted boltzmann machine with prognosibility regularization for prognostics and health assessment

Also Published As

Publication number Publication date
US20180121793A1 (en) 2018-05-03
US10909458B2 (en) 2021-02-02

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